Gretchen Reynolds points
to work by Hug et al.
suggesting that each of us has a unique muscle activation signature that can be revealed during walking and pedaling. Understanding movement patterns could help in improving and refining robotics, prosthetics, physical therapy and personalized exercise programs. On the darker side, a Chinese company (Watrix)
is using computer vision to to enhance the recognition of individuals in crowds by their walking postures:
...its gait recognition solution “Shuidi Shenjian” ... will enable security departments to quickly search and recognize identities by their body shape and walking posture. The company notes that this product is highly effective when targets walk from a long distance or in weak light, cover their faces or wear different clothes, and would be a great supplement to current computer vision products.
Here is the complete abstract from Hug et al.:
Although it is known that the muscle activation patterns used to produce even simple movements can vary between individuals, these differences have not been considered to prove the existence of individual muscle activation strategies (or signatures). We used a machine learning approach (support vector machine) to test the hypothesis that each individual has unique muscle activation signatures. Eighty participants performed a series of pedaling and gait tasks, and 53 of these participants performed a second experimental session on a subsequent day. Myoelectrical activity was measured from eight muscles: vastus lateralis and medialis, rectus femoris, gastrocnemius lateralis and medialis, soleus, tibialis anterior, and biceps femoris-long head. The classification task involved separating data into training and testing sets. For the within-day classification, each pedaling/gait cycle was tested using the classifier, which had been trained on the remaining cycles. For the between-day classification, each cycle from day 2 was tested using the classifier, which had been trained on the cycles from day 1. When considering all eight muscles, the activation profiles were assigned to the corresponding individuals with a classification rate of up to 99.28% (2,353/2,370 cycles) and 91.22% (1,341/1,470 cycles) for the within-day and between-day classification, respectively. When considering the within-day classification, a combination of two muscles was sufficient to obtain a classification rate >80% for both pedaling and gait. When considering between-day classification, a combination of four to five muscles was sufficient to obtain a classification rate >80% for pedaling and gait. These results demonstrate that strategies not only vary between individuals, as is often assumed, but are unique to each individual.
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