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These days, I’m looking at wearable force sensor data and have a thought…
“The pressure values alone are meaningless, but when you run AI, they suddenly interpret it as, ‘This person is currently accumulating fatigue + slightly anxious + putting more weight on their left foot.’ How does that work?…”
Honestly, if you look at raw force data, it’s just a graph with fluctuating load values.
But all the papers talk about “context-aware behavioral interpretation,”
using magic to elevate numbers to meaning, situations, and actions.
So, I have a question for the experts:
How does this actually work?
If you sprinkle some magic powder on force sensor values, can they suddenly become “meaningful behavioral data”?
These days, we see a mix of:
– feature engineering + biomechanical models
– pattern refinement using deep learning
– IMU/EMG and sensor fusion
– these methods.
I’m curious about what works best in practice and in the lab.
Especially force data,
because it’s a character that immediately asks “Who are you?” when activities change, individual weights/gaits vary, and shoes change.
Contextual adjustment is the most difficult…
If you have any practical tips or experiences on how to solve this, please share them.
Raw force data = meaningless numbers
AI/ML = suddenly guessing actions/intentions/situations
→ Please tell me what’s going on here, wise people.
Oh, I totally agree with this post. But honestly, I’m curious: how can AI interpret simple pressure readings from force sensors as “This person is currently accumulating fatigue + putting more weight on their left foot”? Isn’t it just a bunch of numbers?
Exactly. If you look at the raw data, it’s just a fluctuating load graph. The key is how the AI interprets the patterns within those numbers. For example, if it learns that a certain time zone and pressure change pattern are frequently associated with “foot fatigue” or “weight shift,” it derives an interpretation based on that data.
Aha… So it’s not just about looking at the graph, but also learning from that data along with contextual information. But in reality, everyone’s weight, shoes, and gait are different… So, do you have to retrain every time?
That’s the real challenge. That’s why most methods combine “sensor fusion + feature engineering + deep learning.” By integrating it with other sensors like IMUs or EMGs, and extracting individual features, the model can understand the situational variations. Using a personalized model can solve this problem to some extent. While not perfect, it can provide some level of situational adaptation.
Oh, so the key is to use the force sensor in conjunction with other sensors rather than using it alone. Is there an approach that you’ve found most effective in the lab? For example, comparing it to using only deep learning versus a combination of fusion and feature engineering?
Generally, the latter combination is much more stable. Deep learning with a force sensor alone is prone to overfitting, and performance drops sharply with changes in shoes or weight. Fusion, feature engineering, and deep learning provide the ability to identify patterns, reducing the gap between lab and field performance. Empirically, the current trend is “multiple sensors + feature extraction followed by training.”
Great, thanks for the practical tip! So, for future experiments, the key is to collect data from various situations and sensor combinations, and to conduct feature engineering before model training.
