Rumors have been swirling for almost a year now that the next generation of Sony hardware will include both a standard PS6 home console and some sort of handheld device, but according to someone with a whole lot of knowledge and insight into PlayStation hardware launches, that’s a recipe for failure.
Shawn Layden, former chairman of SIE Worldwide Studios, recently appeared on the Trophy Room: A PlayStation Show podcast, and he cautioned his former employer against releasing both a handheld and home console at the same time. Having been with PlayStation through the launch of the PSP and PS Vita, Layden says “it was difficult to support both a console and a handheld at the same time,” adding, “you almost have to create two companies.”
“While they are complimentary, they can also be intention in where you’re going, what you’re trying to say. And the console, which is the bigger dollar, the bigger audience, has more weight in the marketing room. And you see PSP marketing dollars just like go away,” Layden says.
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He then goes into a lengthy anecdote (timestamp) about being admonished by Sony’s CFO for failing to sell enough PS3s even though PSP sales were “off the chart,” but Layden eventually returns to his central point: Sony can be successful in the next console generation, but only if it can focus on supporting just one platform at a time, rather than trying to juggle two distinct machines, i.e. PSP and PS3.
“If Sony only does a handheld next, then I think they have a good chance of winning,” says Layden. “But if they’re going to launch a handheld and a console at the same time, the company just doesn’t have – I don’t think any company has the ability to be successful with both.”
The company said later that day that an investigation two days earlier (on Monday) had revealed that “certain customer information including names, usernames, password hashes and live tokens for all Chainalysis users were exposed to a malicious actor.” It also said it had detected “abnormal activity” and, in response, took down its login page and know-your-transaction service, which monitors threats to cryptocurrency holdings in real time. The company has also removed access to its Reactor and Data Solutions services.
In an update on Friday, Chainalysis said it had begun phasing in customer access to KYT and Address Screening and expected more customers to follow. It asked customers to submit their trusted IP addresses so they can be added to an allow list and to prepare to reset credentials once they regain access. The credential rotation is mandatory.
The company says its investigation has yet to reveal any evidence that any investigation data has been accessed or Chainalysis attribution data has been manipulated.
Several important questions remain unanswered. As noted, the source that made the on-path attack possible is still unknown. It’s not clear how useful the information obtained in such an attack would be unless the attackers also had the ability to decrypt the traffic they were monitoring. If that were the case, the attackers might have gained broad control of the company’s infrastructure.
Chainalysis also didn’t explain why it waited until Wednesday to disclose the data theft of all customers’ personal information when its investigation discovered the exposure two days earlier. Further, the company has provided no details about the AI agents used in the attack and how they worked.
A Chainalysis representative said the company wasn’t commenting beyond the statements in its blog post.
The updated app, rolling out to Xbox Insiders first, launches up to 60 percent faster.
Xbox
The Xbox app for PC is about to get a little faster. Microsoft updated the app for Xbox Insiders on Thursday with performance and navigation improvements, presumably ahead of a full release.
The company says the Xbox PC app now launches up to 60 percent faster in the update, so you can dive into your games with less of a wait. Home, Store and Game Pass pages each load up to 50 percent faster, and windows inside the app open up to 65 percent faster. This is all based on Microsoft’s internal testing, so your experience may vary.
The update also makes the Xbox app’s library easier to navigate, with updated filters and game organization. Search is more unified, covering games, installed titles, people and add-ons in one place. Friends and social features are now part of the main navigation, and the app has a cleaner look, with redesigned game cards and a refreshed Home page and Settings.
Separately, Microsoft says the refreshed Xbox web experience the company previewed in January will soon roll out to more players automatically. (You can try it now at play.xbox.com.) The company is also rolling out a similar redesign to its Xbox cloud gaming apps for TVs and VR headsets.
One of the complications with making different types of videos is the feeling that each one needs its own collection of equipment.
A camera for the desk, another for going out, something for interviews… before long you need a cupboard just to keep track of it all!
In this video the folks at Think Media take a DJI Pocket 4P and Mic 3 through several recording situations, including seated presentations, vlogging, two-person podcasts and using the camera as a webcam.
The interesting part is how they adapt the same basic kit to each job.
There are also some useful reminders about the less exciting side of recording.
Getting the microphone close enough, checking backup audio and making sure the camera stays focused on you are hardly glamorous subjects.
However they become very interesting indeed when you get to the editing stage and discover something went wrong!
The equipment recommendations are a tad enthusiastic, but pay attention to the setup advice as well.
In particular, the focus advice for a seated presenter assumes you stay in that position. Locking focus and then wandering around rather defeats the purpose.
Four Finishing Tools in Filmora 16
Getting everything onto the timeline and in the right order can feel like the end of an edit. Then you play it back and realize there are still a few things that need attention. The color is a little off, the text needs work and that moving graphic seems to arrive with a thud!
This short overview from Wondershare covers four Filmora 16 features aimed at those finishing jobs: Auto Color, track mattes, a separate subtitle track and motion blur for keyframed animation.
The names make some of this sound more complicated than it needs to be. A track matte, for example, lets one track determine which parts of another track you can see. That gives you a way to reveal text through a shape. Motion blur deals with the appearance of movement, while the subtitle track keeps the captions together for easier adjustment.
This is a quick introduction from the software maker, so don’t expect a complete lesson on every tool. It does give you somewhere to start when a project is mostly finished but still has one of those little things bothering you.
Filmora 16 Auto Color: Somewhere to Start
One of the frustrating things about color correction is that knowing something looks wrong and knowing what to do about it are two entirely different things. You can see the footage needs help, but now you have a panel full of controls and no particular idea which one to touch first!
This tutorial from Filmora for Creators covers Auto Color in Filmora 16, which applies an automatic correction to give you a starting point. The useful part is that you can adjust how strongly it is applied rather than having to accept whatever the software decides.
The video covers that strength control, changing it over time with keyframes and saving a setting for later use. So if an adjustment is a bit much for your footage, you can back it off instead of starting again.
Of course an automatic adjustment is still an automatic adjustment. Compare it with the original, have a look at what it has done to faces and familiar colors, and make further changes where needed. The idea is to get you moving without first having to work out every control in the color panel.
How to Animate a Shape Outline in Filmora 16
Sometimes all you need in a video is a simple graphic that directs the viewer’s attention to the right place. A circle appearing around something can do that job without turning the whole screen into a fireworks display.
This short tutorial covers creating a circle in Filmora 16 and animating its outline with Trim Path. Basically, instead of having the complete circle appear all at once, you have the line draw itself around the shape.
The process involves removing the fill, setting up the outline and adding two keyframes to control how much of that outline is visible. There is also an explanation of easing, which lets the movement start and finish more gently.
For anyone getting started with animated graphics, this is a small enough exercise to get your head around without disappearing into a lengthy project. Try it where an explanation needs a visual pointer, then adjust the timing so the circle appears when you actually need the viewer to notice something.
Adding Lyrics with the PowerDirector Subtitle Track
Putting lyrics on a music video sounds like a fairly simple proposition. You already have the words, you already have the music, so just put one over the other and you are done… well, almost!
The part that takes a little care is getting those words to appear at the right time and keeping them readable while the song carries on. In this PowerDirector tutorial Sharper Turtle works through that process using the subtitle track and lyrics prepared in a text document.
He covers adding the phrases, setting their start and end points, and using markers to help with the timing. There are also adjustments to text size, position, borders and shadows, along with breaking lines where they make sense for the listener.
This is a manually timed approach, so there is still some work involved. However having the words organized on the subtitle track gives you a straightforward way to manage it. Get a short section working first and play it back with the music before taking on the whole song.
A Quick Three-Panel Collage in Movavi
If you want several clips playing together in one frame, it is easy to spend more time arranging everything than you originally intended. Sometimes that is time well spent, but sometimes you just want a layout you can drop your footage into and get on with the job.
This short tutorial from the folks at Movavi uses the Vertical Trio Static block to supply a three-panel layout. You replace the example footage with your own clips, then add the Leviathan filter for the darker look described in the video.
The example uses a filter strength of 70 percent, although that is a setting for that example rather than a requirement for your footage. If the important details disappear into the gloom, turn it down!
There isn’t a complicated technique to learn here. It is a quick idea for putting a few related shots together without building the arrangement manually. If you have the block and filter in your installation, that makes it a small project to try for a travel recap or social video.
CapCut’s AI Planning and Editing Workflow
There is certainly no shortage of excitement around A.I. video tools at the moment. The useful question is what happens between having an idea and getting something onto the timeline that actually belongs in your video.
In this video we explore a CapCut workflow connecting reference images, shot planning, generated footage and editing. The example uses a tiger in a snowy setting, carrying the character and surroundings through the process rather than starting from scratch with every shot.
That preparation is the part to pay attention to. If you want a sequence to hold together, the shots need some common direction. Being able to generate another clip doesn’t automatically mean it is the right clip!
The tutorial also covers reviewing results and asking for specific changes. The enthusiasm is fairly high, but there is still an editor making decisions about what stays and what needs another attempt.
Bear in mind that CapCut’s announced tools are at different stages of release, so check what your account can actually access before planning around the complete setup.
A short test sequence should tell you far more about its usefulness for your projects than another collection of impressive promises.
Building a Documentary-Style Animation with Claude and Remotion
It is tempting to look at an A.I. assisted video and assume someone typed a request, pressed a button and went off to make a cup of tea!
In this tutorial from Dina and Agnes you can see you a rather more involved and more accurate picture of the process.
Dina works through a fictional documentary-style video, starting with the script and moving through revisions, a storyboard, images, narration and animation.
What makes this useful is the preparation before the animation begins. Each part of the script needs a visual, those visuals need assets, and the animation needs timing.
Claude Code and Remotion handle the animation work, with narration and a subtitle file providing the timing information. CapCut is also used in the production and finishing process.
There is a fair bit to set up here, including a paid Claude plan, Node.js, Git and the other tools involved. It is more suited to readers comfortable getting those pieces working together.
However the planning is worth understanding even if you aren’t ready to tackle the software.
Giving the system a clear sequence to build, then asking for specific corrections, is a much more useful starting point than hoping it guesses the whole thing correctly.
Instagram Long-Form Video: Something to Keep an Eye On
Whenever a platform starts experimenting with a different video format, the temptation is to immediately work out how much of your existing strategy needs changing. Usually there are a few fairly important questions to answer before getting that excited!
In this video from Content Creators they discuss a reported Instagram test called Features, involving longer videos and short Reels that can lead viewers to them.
For anyone already making interviews, tutorials or podcasts, the possibility is another place to use work you are already producing.
They also raise questions that make this worth approaching carefully.
Will people stay to watch? How will they find the longer video? What happens with monetization? At this stage, access is limited and the answers are still developing.
If you have access, a small experiment using an existing video makes more sense than reorganizing everything around a test.
Choose an excerpt that offers something useful on its own and gives the viewer a reason to continue to the longer piece.
There may be an opportunity here, but an announcement and an audience are two different things. Keep an eye on it without assuming your established YouTube plans suddenly need replacing.
Slow Motion and Speed Ramping in DaVinci Resolve
Slow motion is one of those effects that looks simple enough until you try it on footage that wasn’t really shot for the purpose. You slow the clip down and instead of a dramatic moment, you get something that looks as though the video is struggling to play!
In this tutorial Casey Faris starts with the relationship between recording frame rate and playback speed before moving into the controls in DaVinci Resolve. Having enough recorded frames gives the software more to work with when you want the action to play slowly.
He then covers speed points and Retime Controls, using a skateboard clip to move between faster and slower sections. The curve adjustments are worth paying attention to because they let the change happen gradually instead of having the footage suddenly lurch into another speed.
There is also coverage of Optical Flow and A.I. Speed Warp, which create extra frames to fill the gaps. Speed Warp requires the Studio version, and pushing these tools too far can produce odd-looking details. They still have limits!
For your own footage, first decide which moment deserves the emphasis. Then build the speed changes around that moment. And if you know you are going to want slow motion, make the frame-rate decision while shooting rather than leaving the whole problem for the edit.
Giving Your Audience a Reason to Come Back
There is a lot of advice around telling creators to get viewers to do something. Comment, subscribe, click, buy… somewhere in all of that it is worth remembering that the viewer probably arrived hoping to get something useful out of the video!
In this video Chris discusses moving away from constantly pushing the audience and concentrating on being helpful.
He describes receiving more interest in his work after making that change, although that is his experience rather than a promise about anyone else’s sales.
The practical starting point is to identify who you are helping and what you are helping them achieve.
Once that is clear, each video can offer a useful step in that direction instead of trying to persuade everyone to do everything at once.
He also questions whether chasing trends and cutting out every pause really helps if the result loses the personality of the person presenting it. Getting to the point is useful. Sounding as though you are racing a stopwatch isn’t automatically an improvement!
For an educational channel or a business using video, this provides a useful way to think about the next upload. Give the viewer a reason to remember you before worrying about what you want them to do afterward.
Key Takeaways
Think Media demonstrates how to adapt a DJI Pocket 4P for various video scenarios, emphasizing setup and equipment care.
Filmora 16 offers features like Auto Color and motion blur to enhance video finishing, providing starting points for edits.
CapCut connects AI planning with editing, allowing for generated footage while still requiring thoughtful direction.
The process of building documentary-style animations using Claude and Remotion highlights preparation’s importance before animation starts.
Creators should focus on delivering value to viewers instead of just pushing for actions like subscriptions, thus fostering engagement.
The Role of Predictive Analytics in Shaping the Future of Healthcare
How AI-Driven Predictive Modeling in Healthcare Helps for Personalized Treatment?
The health care industry is also making a paradigm shift with the facilitation afforded by advancements in AI technology and health information systems. One-size-fits-all solutions are being relegated to the backseat with the emergence of personalized medicine, with treatment tailored using the power of enormous volumes of health data.
Predictive modelling anchored on Artificial Intelligence (AI) is at the center of this revolution, an elevated methodology consisting of Machine Learning (ML) applications, current patient information, and physician knowledge to forecast outcomes. The technology is revolutionizing digital health solutions by giving the capacity to diagnose sooner, enhance treatment planning, and attain improved long-term outcomes.
The Role of Predictive Modeling in Personalized Healthcare
Predictive modeling in the health sector uses AI and ML algorithms to analyze massive quantities of genomic data, Electronic Health Records (EHRs), and real-time streaming sensor data from wearables to predict future health events. Conventional statistical models cannot learn from unstructured data (e.g., radiology images or clinical narrative), identify hidden patterns, or improve predictability over time.
Multimodal Healthcare Data Analytics
Multi-source health data is the key for AI models, all from EHRs, medical images, genomics, and wearable sensor data. Advanced ML models search through all this rich data to build treatment insights specific to a given patient.
For example, deep learning algorithms can analyze MRI scans, gene mutations, and patient information and suggest individualized oncology treatment administrations. IBM Watson Health is the best example that demonstrates predictive analytics healthcare augmentation of clinician judgment with unparalleled accuracy.
Real-Time Monitoring and Time-Series Prediction
AI and predictive analyticsin digital health also perform well with real-time patient tracking. Real-time predictive algorithms match physicians and hospitals by monitoring physiological information such as heart rate, oxygen saturation, and blood pressure to identify anomalies and predict impending health emergencies.
Explainable AI (XAI) in Clinical Settings
With improvements in healthcare AI, transparency has never been more critical. Explainable AI provides clinicians immediate access to the reasoning behind the decision-making process, whether it’s the identification of the most important risk factors for disease or why one particular treatment is necessary. Such a trust-oriented focus is key to clinician adoption and regulatory approval of precision medicine AI technology.
Federated Learning for Secure AI Training
Personal data privacy is of the highest value in healthcare. Federated learning allows one to train AI models across decentralized data silos without compromising patient confidentiality. This keeps it under data governance and adds more to the availability of AI in healthcare data analysis.
Key Applications of AI Predictive Modeling in Personalized Healthcare
Early Diagnosis and Risk Stratification
Early disease diagnosis is the biggest application of predictive modeling. The scope of AI prediction in detecting early signs of diseases like cancer, cardiovascular disease, and neurological disease from EHR processing, lab data, and imaging studies weeks or months before presentations is enormous.
For example:
AI machines forecast breast cancer through mammography with fairly accurate percentages, enabling early treatment.
AI software calculates the chances of heart failure or diabetes in a patient and takes protective measures in advance.
Early diagnosis enhances prognosis, minimizes treatment stresses, and minimizes healthcare expenses.
Personalized Treatment Plans
Personalized treatment plans are now being prescribed by AI solutions based on individual patient’s profiles. They include:
Choice of the right drug from a genetic match
Optimization of optimal dosing
Prediction of drug response or resistance
As an example, in treating cancer, AI systems take into account the genetics of the cancer to recommend customized chemotherapy regimens. AI-powered clinical advice aids practitioners in choosing drugs that are best suited for a patient’s medical history, rather than doing it through trial and error.
Chronic Disease Management
Asthma, diabetes, and hypertension are conditions that must be continuously monitored. AI predictive models can:
Predict complications
Enforce lifestyle modification
Real-time data are accurate and come from smartphone applications and wearable sensors. This data is input into AI models to derive insights into treatment planning, improving quality care management.
Reducing Hospital Readmissions
There is also predictive modeling to identify high-risk readmission patients. This data is utilized by the hospital in developing post-discharge care plans, telemedicine follow-up, and patient-specific education, thus enhancing patient outcomes and minimizing readmission penalties.
Genomic Data Integration for Precision Medicine
Genomics is also taking us to personalized medicine. Genomic sequence screening can be combined with AI for:
Detection of disease-causing mutations
Genetic risk prediction
Targeting of cancer therapy
For example, predictive modeling aids in the choice of immunotherapy with genetic biomarkers like expression of PD-L1 in lung cancer.
Optimizing Clinical Trials
It will be a failure to enroll patients into clinical trials. AI makes it a success because:
EHR and genotypic matching of potential enrollees
Prediction of patient response to trial medications
Prevention of dropouts Congestion-free reduction of trial duration and rapid-track new drug development.
Use Cases of Predictive Modeling in Healthcare
Oncology: Tailored Cancer Care
Personalized Cancer Treatment: Predictive modeling is revolutionizing treatment and diagnosis in AI oncology solutions.
Treatment Option: Based on disease history and gene markers, AI gives chemotherapy, targeted therapy, or immunotherapy to a patient with cancer.
Radio Genomics: Machine learning integrates radiology images and genomics to forecast cancer aggressiveness and treatment response.
Clinical Trial Matching: Predictive analytics identifies the patient and matches them to trials, and AI-assisted drug development is made possible.
Cardiology: Smart Heart Health Management
AI models review ECG records, blood cholesterol readings, and patient histories to predict the likelihood of stroke or heart attack.
Predictive modeling also powers virtual heart twins for test therapy simulation before use.
AI-powered diagnosis of arrhythmia by wearable technology, the basis of remote patient monitoring solutions.
Neurology: Early Detection of Brain Disorders
Early detection and monitoring are made possible in neurology via AI by:
MRI, genetic data, and behavioral patterns to identify Alzheimer’s and Parkinson’s.
Stroke triage in minutes with AI tools such as Viz.ai, scanning for lethal blockages in the brain and alerting specialists in real-time.
These are only some of the many ways machine learning in medicine allows physicians to respond preventively.
Mental Health: Proactive Interventions
Artificial intelligence-based Natural Language Processing (NLP) and sentiment analysis identify early depression, anxiety, or suicidal thoughts via social media, online conversations, and SMS.
Predictive analytics also serve to assess the risk for chronic patients with bipolar disorders or schizophrenia.
Predictive analytics is revolutionizing disease management of hypertension and diabetes. Some of the top applications are:
Use of metabolic twins such as Twin Health to help with individualization of treatment and lifestyle for diabetic patients.
Predicting non-adherence to drugs based on history of behavior, socio-economic status, and health literacy.
MIPD-based CG dosing platforms enable patients to receive medication doses that are responsive to their genetic profile and the rate at which they metabolize.
Real-World Success Stories in AI-Powered Personalized Treatment
Predictive Oncology Enhancing Survival Rates
Predictive Oncology’s AI platform recently demonstrated improved survival prediction in ovarian cancer patients, supporting the potential of AI in personalized cancer therapy.
Sepsis Mortality Reduced with AI
UC San Diego Health recorded its COMPOSER program decreasing sepsis mortality by 17%. Real-world testing vindicates the potential for life-saving predictive modeling in critical care.
AI-Guided Cardiology
Machine learning technology used for coronary artery disease has prevented side effects and hospitalizations by detecting risk patients earlier than ever before.
Personalized Side Effect Prediction
The AI algorithm developed in the UK is able to predict three years later the likelihood of lymphedema among breast cancer survivors, promising a new era of individualized post-treatment possibility.
Improved Accuracy: AI models are superior to traditional statistical methods in handling high-dimensional data. This improves diagnostic and treatment prediction accuracy.
Scalability: AI models, once trained, can handle large amounts of data from diverse sources and scale across different departments, hospitals, or groups.
Real-Time Insights: AI can enable ongoing, real-time insight into the patient’s situation so that action can be taken immediately.
Cost Reduction: Prevention and anticipation of illness in advance save costs for treatment, prevent complications, and reduce hospitalization.
Patient Empowerment: Individuals are offered personalized care plans and proactive management of health recommendations, and they become engaged participants in their own health process.
Future Trends: What’s Next for AI in Personalized Medicine
Digital Twins Across the Human Body
From heart to kidneys to pregnancies, digital models of patients will eventually enable simulation of disease progression and treatment response.
Predictive AI in Population Health
Not only patients, but predictive analytics will forecast disease epidemics, streamline hospital resource use, and support value-based care models at scale.
Generative AI in Drug Discovery
Generative Artificial Intelligence is speeding up drug discovery through protein structure prediction and drug interaction modeling, driving hyper-personalized care.
Blockchain in AI-Powered Health Systems
Health technology will ultimately meet up with decentralized technologies to provide secure and transparent management of individual healthcare data.
Conclusion
Put digital health, predictive analytics, and artificial intelligence technology together, and personalized medicine is a reality. In oncology, cardiology, neurology, mental health, and chronic care, AI enables clinicians to deliver care according to the unique profile of each patient. Over the next coming years, AI for healthwill augment but also foretell, adapt to, and customize, rewiring the patient experience around every bend.
Today’s post covers the human factor principles for ergonomic product design services. Perhaps the single most important approach to designing commercial products is ergonomics, as it has everything to do with usability, which then directly impacts profitability. Ergonomics revolves around the notion that a tool, device, or product of any sort must be designed to accommodate the user’s needs instead of the other way around. It’s an idea of “fit for use” applied across the sensory domains, both cognitive and physical, based on the understanding that human factors play crucial roles in the product design process.
An ergonomically bad design may lead to physical strains, unnecessary cognitive load, and overall decreased comfort and impaired productivity. On the other hand, great ergonomics makes a product enjoyable to use and improves the experience as a whole. Designers and engineers must aim to build a product that’s safely and easily usable by the vast majority of people or at least the larger portion of the target demographics. They take into account the size, shape, looks, and feel of the product to make it as user-centered as possible. In other words, the product development project needs to put the user at the center of the design process with an intention to optimize accessibility. Ergonomics plays a huge role here as it serves as a design guideline to create a product that’s convenient, flexible, and welcoming.
Convenient because it’s usable by everyone with minimum effort, flexible because it’s adaptable to suit individuals’ preferences, and welcoming to the point where there’s little to no barrier to usage. The scope of ergonomics isn’t limited to physical interaction between the user and the product, which has everything to do with human anatomy. It also covers cognitive processes in product use such as reasoning and perception. Cognitive ergonomics may include the layout of clear text and symbols, intuitive user interface (important when interaction with a product happens based on information displayed on a screen), colors, etc.
According to Henry Dreyfuss, an American industrial designer best known for his works on the Honeywell T87 Round Thermostat and the Western Electric Model 500, products are always meant to be used by people in so many different fashions. Products might be sat upon, looked at, ridden in, interacted with, activated, and basically operated for usage. When the point of contact between the user and the product turns into a point of friction, the industrial designer has failed.
Conversely, if the point of contact makes people more comfortable, eager, or simply happier to use and purchase the product, the industrial designer has succeeded. This particular “point of contact” is obviously about ergonomics, which is firmly grounded in anthropometrics. Every good designer worth their salt has to take into consideration the relationship between the two, so they can successfully implement it in their design.
Anthropometrics
It’s all about the measurements of the human body. Within the context of design, anthropometric data of the target demographics serve as points of reference, ensuring that the shape and form of a product are suitable for the people intended to use it. These data may include physical characteristics like height, weight, grip size, circumferences (head, neck, chest, waist, hip, etc.), limb lengths, reach, and more.
A lot of critical design decisions in new product design are often based on anthropometric statistics. For example, designing a handle for a lawn mower is largely based on the hand width and grip size data; building a workstation takes into account eye heights when a person is in a seated position so the monitor placement is appropriately safe and comfortable for the vast majority of users; the shape of a helmet must be designed to fit the wearer’ head circumference; car interior must allow comfortable seating with enough headroom, legroom, and reach envelope of the driver and passenger, and so forth.
But remember that manufacturers and designers want the product to be usable by as many people as possible, because that’s how they measure success. At the same time, it’s difficult to build something that every single person will be able to use with comfort. People have different heights, weights, and other physical characteristics. The anthropometric statistics are typically divided into categories as follows:
The 5th percentile is 5% of people with smaller physicality
The 50th percentile is people of average size
The 95th percentile is 5% of people with larger-than-average size
For most NPD (New Product Development) projects, designing only for the “average” or the “50th percentile” category is often not enough, as it can be too restrictive in terms of marketability. A more robust approach is to design something that will accommodate the entire range, from the 5th to the 95th percentile, to improve the chances of the product fitting nearly the entirety of its intended users. Here is a common example of what anthropometric data (for adults) looks like:
Body Parts(Adults Aged 19 to 60)
5th percentile
50th percentile
95th percentile
Male
Female
Male
Female
Male
Female
Hand length
180 mm
165 mm
192 mm
179 mm
211 mm
198 mm
Handbreadth
85 mm
71 mm
90 mm
78 mm
98 mm
85 mm
Forearm to hand length
446 mm
406 mm
480 mm
442 mm
504 mm
482 mm
Elbow to wrist length
266 mm
237 mm
289 mm
262 mm
316 mm
283 mm
Anthropometric data is a crucial guide to ergonomic designs. Without detailed and accurate measurements of the physical characteristics of the target users, the resulting product might end up causing discomfort and pain at best, or injuries at worst. Sometimes ergonomics isn’t just the size and shape of the product. The materials, weight, textures, and positioning of buttons and controls also contribute to the overall user experience.
As self-explanatory as it might be, physical ergonomics primarily concerns the human physiological, anatomical, and biomechanical characteristics. Put within the context of product design, this translates into creating an object that can accommodate the user’s natural movements, physical strength, dimensions, and limitations.
Take the design of a handheld tool or device, for example. Proper physical ergonomics requires the size and shape of the handle as well as the weight of the entire device to be just right to ensure a secure grip while minimizing muscle strain. If the device also has switches and buttons for control, the positioning and dimensions of the parts also contribute to physical ergonomics. Whether the electronic device is supposed to be operated using one hand or two hands, every button must be easily accessible without losing grip on the device itself.
Here are a few more examples:
A designer is working on an emergency stop button to be installed in an industrial facility. Like every emergency button, it has to be clearly visible and must allow quick pressing with minimum effort using the entire palm of the hand. Based on the anthropometric data above, the designer finds that the handbreadth measurements of the users’ demographics stand between 71 mm (smallest, female) and 98 mm (biggest, male). If the button is supposed to be easily and quickly operable using the entire palm, it needs to be at least 98 mm in width (or diameter, depending on the shape). An emergency button smaller than the handbreadth of the 5th percentile of the population might not be as accessible. A larger button than the handbreadth of the 95th percentile can work just fine, but it’s not a cost-efficient approach to design.
A design engineering expert is designing safety gloves for everybody in the factory to wear. Instead of trying to make multiple sizes of gloves, the factory decides that it can only make one-size-fits-all models because of budget constraints. Using the information from the anthropometric table, the hand lengths of the population are from 165 mm (shortest, female) to 211 mm (longest, male). Based on the data, the engineer determines that every pair of gloves needs to have a minimum length of 211 mm, so that it may accommodate the majority of the population. Some workers might find the gloves too big, but still it’s a better compromise than wearing gloves that don’t fit at all.
Again, anthropometric data is crucial, but biomechanics isn’t something product designers can just ignore either. Among the most important considerations are posture (when using the product, such as standing upright or seated), how the user is expected to interact with the object or product, the repetitive movements involved in the interaction, and the amount of force required to operate the product. With all of those in mind, physical ergonomics demands that any kind of interaction with the product must feel natural and comfortable to the user. Biomechanics guides the designer to build a product that works with (rather than against) human physicality. Another one of the most commonly cited examples of physical ergonomics design is a person sitting at a computer desk.
It measures just about everything that may affect the person’s comfort level at work. Seat height must be adjustable so the person’s feet can lie flat on the floor while sitting down, without restricting access to the desktop. The sitting position itself should be flexible by adjusting the backrest and armrests because the correct sitting position with good posture allows for a better viewing angle of the screen and minimum strain on the body. The table height should allow the person to reach the keyboard and mouse with minimum strain on the hand and shoulder. Some computer desks offer height adjustability as well. Distance between the desk and the chair also matters because the user shouldn’t have to stretch the arms to reach the keyboard, mouse, and possibly other peripherals. The monitor height and viewing angle make it easy for the person to see the screen without any discomfort in the neck, eyes, and back.
About clearance and reach
Physical ergonomics isn’t always about the shape and form of a physical product and its relation to human biomechanics. In many cases, it also touches on the subject of distance, or more specifically “clearance” and “reach.” Think of clearance as an opening or a doorway where a user needs to go through to reach a specific area. In the context of physical ergonomics, clearance is the minimum dimension of the opening required to allow a user to enter or exit safely. In real-world scenarios, the term “clearance” might need further definitions depending on the specifics of the applications. Product engineers should consider the following definitions:
The vertical space between the floor and the lowest positioned overhead obstruction (doesn’t have to be the ceiling). A safe clearance means the minimum available vertical space that allows the tallest user to go through in a safe and comfortable fashion. When measuring the height of a user for this purpose, it must include any footwear and personal protection equipment.
The horizontal space between the right and left walls, or between obstructions that may prevent a person from going through an opening. A safe clearance is the safe minimum distance that allows the widest person (plus room for comfortable movement) to enter or exit an area. Horizontal clearance must be designed with inclusivity in mind. For example, a person in a wheelchair might require wider horizontal clearance.
The distance between an unprotected hazard and an accessible point. Safe clearance refers to the fact that the accessible point is still well clear of the hazard even for the tallest person with the longest limbs (think of the guardrail that separates an area from large rotating machinery in a factory). In some cases, clearance can also mean the width of an opening that prevents users or their body parts from entering a hazardous area, such as grill openings in fans or computer cases.
Reach, meanwhile, is the three-dimensional space around a user’s position in any given location. If a user is in a seated position at a desk, the reach is as far as the arms and legs can stretch out without moving to a different place. Also commonly referred to as “workplace envelope,” the idea behind this is for the product concept designer to design every product or object to ensure comfortable reach for the user. Unlike with physical ergonomics, where a product is designed for everybody from the 5th to 95th percentile, the workplace envelope is geared towards the 5th percentile, because then it also means everybody else with larger physical characteristics can operate the product within the envelope.
Quite a lot of products are ambidextrous, or in other words, designed for both left-handed and right-handed users without add-ons or modifications. Pencils, rackets, balls, cellphones, remote controls, water bottles, kitchen tools, and many other handheld devices in fact have ambidextrous designs. Still, the large majority of products are designed primarily for right-handed users. Think of common everyday products like scissors, ballpoint pens, power tools, typical computer mice and keyboards, cameras, manual can openers, motorcycles, guitars, the lot. DSLR cameras are probably the most obvious example, as the shutter and most control buttons are positioned for right-handed users.
But this doesn’t mean that products can’t be made in an ambidextrous fashion. One of the biggest offenders when it comes to DFM services in this matter, is a ruler. For left-handed users, the conventional ruler is a pretty annoying instrument. But then again, it’s also the case with ballpoint pens and the left-to-right writing direction of Latin script. Ballpoints are supposed to be pulled across the page using the right hand, not pushed forward using the left hand. A truly physically ergonomic product shouldn’t exclude any user. While this isn’t always possible with every product, a ruler is almost definitely a good candidate for ambidextrous design.
Standard rulers have the measurement scales oriented for left-to-right reading that naturally aligns with right-handed use. When used by a left-handed person, the scales might appear inverted or generally awkward to read. An easy solution would be two-sided rulers where the scales are written on both sides; one side for right-handed use, and another for left-handed people. If the users are not ambidextrous, they can just flip the ruler around for the most comfortable use. But no left-handed people are complaining, at least not as loudly as it could be simply because they’ve learned to live with how things work. And for companies and 3D designers, it’s never really a question because around 90% of the world’s population is right-handed anyway.
If physical ergonomics concerns design for the body, cognitive ergonomics is about designing for the mind. Instead of focusing on the physical interaction between the user and the product, it revolves around the idea of mental processes being an influential factor that affects usability and comfort. Key considerations include attention, memory, reasoning, motor response, and perception. Cognitive ergonomics is often cited in discussions about digital products, especially the UI (user interface) in software, applications, or website development, but it is also perfectly applicable in the design of physical products.
Even if a product doesn’t have a screen or digital interface of any sort, cognitive ergonomics maintains its relevance to more conventional control systems such as the layout of buttons, the use of text or symbols, and feedback mechanisms. An easy example is keyboard layout. Related buttons are grouped together in their own specific column and rows, such as the Fn (Function keys at the top and additional control keys (Caps Lock, Shift, Esc, and Enter in the left and right columns. If the keyboard has a dedicated Numeric Keypad, it’s usually isolated on the right-hand side.
The idea is to reduce the mental workload from operating the product and minimize chances of mistakes because the interaction feels natural and intuitive (although admittedly the Num Pad on the right-hand side doesn’t favor left-handed users). The use of standardized icons or symbols, clear auditory and visual feedback with soft blinking lights or beeping sounds, and appropriately sized buttons are all parts of cognitive ergonomics.
Closing thoughts
A major part of physical and cognitive ergonomics design is inclusivity. 3D Product designers should aim to build a product that can be easily, comfortably, and beneficially used by as large a target demographic as possible, including people who may otherwise be marginalized. It’s true that companies often target users categorized between the 5th and 9th percentile, but this doesn’t mean anyone outside the groups is excluded. But because designing specific sizes, shapes, and specifications for the outliers can be expensive in mass production, designers may devise adjustability options or other accommodations such as add-ons or extensions for the standard product to save cost, without negating the usability aspect.
Ergonomics analysis, both physical and cognitive, based on anthropometric data has always been an integral part of the product development process at Cad Crowd. For more than 15 years, the platform has served the industry as a major design hub to connect clients with some of the experienced industrial designers, engineers, and NPD professionals from all around the world. Contact us for a free quote today.
MacKenzie Brown is the founder and CEO of Cad Crowd. With over 18 years of experience in launching and scaling platforms specializing in CAD services, product design, manufacturing, hardware, and software development, MacKenzie is a recognized authority in the engineering industry. Under his leadership, Cad Crowd serves esteemed clients like NASA, JPL, the U.S. Navy, and Fortune 500 companies, empowering innovators with access to high-quality design and engineering talent.
Fortnite’s new Treasure Hunters season is such a massive success that the battle royale’s player counts are five times higher than last week, because everybody and their mothers want in on the search for Tim Sweeney’s golden ticket. It grants everything in the item shop for free, both for two weeks this year and two more weeks in 2027. That means two things: Most of the people playing Fortnite right now haven’t been playing recently, and folks aren’t necessarily playing to win.
The way the hunt works is that every time you or one of your teammates opens a treasure chest, there’s a chance that a rare prize will be granted to the whole party. It could be a golden A$AP Rocky skin, a Mercedes G-Wagon skin, or the extremely elusive Pirate Shop Access token, which makes virtually the whole shop free. The hunt for that Pirate Shop token in particular has changed the way people play BR. The primary goal for many players is now not to win, but to open as many chests as possible in pursuit of the token.
Since your whole squad gets any of these treasures regardless of who found them, four-player Squad fills have been the place to go for the hunt–understandably, most of us don’t have enough friends who want to play Fortnite 12 hours a day to keep the squad filled on our own. Playing Squad fills has always been a bit of crapshoot in terms of the quality of your teammates, but the new treasure hunt has folks acting a lot more erratic than usual.
While playing entirely too much Fortnite this week, I’ve identified a few player profiles–let’s call them recurring characters–that I’ve been encountering over and over again. While your teammates each game are randomly assigned, they typically follow one of a handful of archetypes during the hunt. I’ve played about 100 games of Squad fills since the new Treasure Hunters mini-season began on Sunday, and nearly all of my teammates ended up being one of these types of players.
The Five People You Meet In Fortnite’s Treasure Hunt
The Looter
This person is on a mission to open as many chests as they possibly can, and they will ignore everything else going on during the match. They might land near you, but they won’t help you if you get into a fight, because they have something more important to do. A lot of these folks have so little desire to engage with the actual game that they end up hanging out deep in the storm, looting by themselves for most of every match. These types sometimes come in pairs.
The Loner
This person will land as far away from the rest of the team as they can, and then they’ll proceed to play the whole match as if they were in solos because they think the key to min/maxing the squads chest-looting is to spread out as far as possible. If they don’t die immediately, you’ll forget they exist until the rest of your team wipes and you all end up spectating this guy doing whatever.
The Hunter
This person is the Looter’s natural predator. They just want to get into fights, and they know this influx of new players and folks who haven’t played in a while, most of whom are more interested in looting than fighting, is a great opportunity to pad their stats. But these folks are so aggressive that they either end up running off alone and wiping the lobby by themselves or getting killed quickly–but it’s usually the latter.
The AFKer
This type just shows up and does nothing at all. They drop from the bus at the end of the line and float in the water until the storm kills them. During any other season, we would assume this kind of behavior is an accident, but it’s happening too often for that to be the case right now. These folks are here to let other folks farm chests for them.
The Regular Player
There are still some people who play Fortnite the usual way, sticking together and looting carefully and playing strategically as a team in hopes of pulling out a win. These folks know that the chance of getting a Pirate Shop token is infinitesimally small, and so they largely just play the way they normally would and don’t worry about the prizes. With how preoccupied so many people are with the Treasure Hunt, if you manage to pull a squad with two or three of these folks, you’re probably going to win the round.
Fortunately, this state of affairs won’t last forever. The Pirate Shop token will only be around during this One Piece-themed mini-season, which only lasts until December 5. Let’s hope the Treasure Hunt doesn’t burn everyone out before Chapter 8 starts.
At least 10 local governments and police departments have moved since this summer to curb, restrict, or end their use of Flock Safety technology as concerns about mass surveillance and data privacy continue to grow. The backlash is part of a broader pushback against the rapid adoption of advanced AI technology, including the data center buildout, facial recognition, and other uses of AI.
Flock, founded in 2017, sells surveillance software — like license plate readers, drones, and microphone-based edge-computing devices to pick up gunshots and other sounds — that can help solve crimes, such as finding missing people or stolen cars. More than 100,000 Flock cameras are in use nationwide.
But in the past three months, Monroe, Ohio; Newburgh, New York; Richmond, Virginia; Hopkinton, Massachusetts; New Bedford, Massachusetts; and Lansing, Michigan, have all moved to end their contracts with Flock. Meanwhile, police in East Bridgewater and Grafton, Massachusetts, also announced plans to stop using Flock cameras. Just this week, residents of League City, Texas, voted against their police department’s continued use of Flock’s license plate readers, and the cameras are now set to be removed.
The concerns range from privacy to how data is shared (and with whom) to procurement concerns.
In Asheville, North Carolina, police lost access to Flock after the city voted to terminate its contract in August, and the El Paso County Sheriff’s Office in Colorado announced in September that it would stop using Flock cameras. When asked for comment, the El Paso County Sheriff’s Office redirected TechCrunch to its press release, where Sheriff Joseph Roybal said that discontinuing the use of Flock was the right decision for the county.
“We have the tools, training, and experience to locate and apprehend criminals without relying on Flock cameras,” he said in the press release.
Tatiana Rice, a senior director for U.S. legislation at The Future of Privacy Forum, told TechCrunch that the backlash to Flock isn’t surprising and only further represents the “growing public skepticism toward technology.” Flock is a target because people can see the sensors and cameras as they drive along roads; data centers have also become physical embodiments of the resentment growing against emerging technology. “A camera at the end of your street is tangible in a way that everyday sharing and selling of personal data online is not,” Rice said.
At the state level this summer, for example, Florida barred license plate readers from state highways, while Texas paused state funding for Flock. The issues again range from data privacy concerns to misuse of the technology.
Misuse has been a real problem. In August, for example, an officer in Brentwood, Missouri was accused of using Flock cameras to stalk his ex-wife during their divorce. In September, an officer from East Texas was sentenced to prison for using Flock to stalk his ex-girlfriend and her family.
Another growing concern is how immigration officials have been able to access the technology. One case that captures the issue came in March, when Ithaca, New York, passed a resolution to terminate its contract with the Flock Group.
In the resolution, the city said there were “proven instances of Flock Safety infrastructure being used by federal agents for information purposes even without specific authorizations from the contracting locality,” and that the city had reviewed reports that “either intentionally or through its negligence,” Flock had allowed U.S. Immigration and Customs Enforcement (ICE) and the Bureau of Alcohol, Tobacco, Firearms and Explosives (ATF) to “use query results from its database of state and local ALPR [automatic license plate reader] data to investigate and persecute immigrants throughout the United States.”
“Every day, police departments across the country are using Flock technology to solve serious crimes, locate suspects, find missing people, and make a measurable impact on public safety,” Flock told TechCrunch in a statement. “While we respect every community’s decision, removing a tool that is actively helping solve violent crimes and protect communities moves public safety backward. Every Flock customer owns its data and controls who it is shared with and how it can be used. Flock does not sell data and does not have a contract with ICE.”
This August, Flockannounced it would shorten the default period it stores data for new customers, from 30 days to seven. It also said agencies now need a case code before they can search the database.
The conversation around Flock echoes, in many ways, the fight overdata centers. Many of these cities adopted Flock just a few years ago, and residents likely had no idea it was being installed, Adam Schwartz of the Electronic Frontier Foundation, a digital privacy nonprofit that has investigated Flock before, told TechCrunch.
That lack of awareness is similar to how data centers are popping up across the country, often before residents understand what is happening. Schwartz said police departments often decide to use Flock on their own, with little oversight from city councils. In South Windsor, Connecticut, according to local reports, Flock cameras went live last year without public warning or a vote. In October, the town voted to remove its Flock cameras.
“Grassroots activists across the country have been demanding that city councils exercise their procurement power to refuse to pay for ALPRs,” Schwartz said, referring to councils’ power to decide what the city buys. He added that activists have been fighting for years and that “many city councils are listening.”
The main argument for technology like Flock is that it helps combat crime and keep neighborhoods safe. And many local governments are not dropping Flock or similar technology; instead, they are adding restrictions on its use or looking for alternatives.
Cleveland, Ohio, for example, voted this summer to extend its contract with Flock, but has started restricting searches related to immigration. It has also limited access to the database to the Cleveland Police alone, cutting off the Northeast Ohio Regional Fusion Center, essentially a federal intelligence agency, which had initially been given access.
“They are used and needed for investigative purposes,” the city said when reached for comment. “The Department of Public Safety is currently reviewing responses to an RFP [request for proposals] that was sent out to other vendors that provide the same technology as Flock.”
But as with data centers, communities want more say in the trade-offs. “These tools can offer real public safety benefits, but communities are clearly interested in ensuring that those benefits are not at the cost of their civil liberties,” Rice said. Surveillance technology will likely remain a political hot topic, especially since Democrats now control Congress.
In September, President Donald Trump said he liked Flock’s technology “because of law enforcement,” even though a Reuters poll that same month found that more Americans dislike the technology than like it.
Also that month, Rep. Thomas Massie, a Republican from Kentucky, introduced a bill called the Flock-Off Act to bar the use of federal money to buy “Flock-style surveillance” and put other safeguards in place. In October, Sen. Bernie Sanders (I-Vt.), Rep. Alexandria Ocasio-Cortez (D-N.Y.), and Sen. Jeff Merkley (D-Ore.) introduced the Ban Flock Act to end “the rise of AI-powered surveillance cameras.”
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The FAA announced today that it is finally phasing out floppy disks from air traffic control systems at more than 400 facilities. Engadget reports: They’ll be replaced by a more modern system called Enterprise — Information Display System (E-IDS), which is comprised of a digital display for relaying status information, updates and other alerts to air traffic teams. The FAA plans to have E-IDS installed in 435 sites by the end of 2028; the first five locations to receive the upgrades are the Atlantic City International Airport, Nashville International Airport and Greater Rochester International Airport, as well as data processing hubs in Atlanta and Salt Lake City.
Kirby™ Air Riders brings the beloved puffball hero to the skies in a thrilling vehicular action-racing experience exclusively tailored for the Nintendo Switch 2. Developed by Nintendo EPD and published by Nintendo itself, this title transforms the classic Kirby franchise into an adrenaline-fueled aerial battle where players can choose from a variety of riders and machines to compete in high-speed races, arena battles, and strategic challenges. Death at Fleming Manor
The Nintendo Switch 2 platform enhances the experience with its powerful hardware, delivering smooth 60fps performance in both docked and handheld modes while maintaining vibrant visuals and responsive controls. Whether you’re racing through the bustling streets of Skyah or battling in the grand stadium, the Switch 2’s enhanced graphics and optimized performance ensure that every spin, dash, and aerial maneuver feels crisp and immersive. The game’s unique blend of racing, combat, and Kirby’s signature Copy Abilities creates a dynamic gameplay loop that rewards both skill and strategy, making it a standout title for fans of fast-paced action and multiplayer competition.
