Force Sensitive Resistor
HUMAN GAIT ANALYSIS
Abstract:
Sensible Shoes is a hands-free and eyes-free foot-computer interface that supports on-the-go interaction with surrounding environments. We recognize different low-level activities by measuring the user’s continuous weight distribution over the feet with twelve Force Sensing Resistor (FSR) sensors embedded in the insoles of shoes. Using the sensor data as inputs, a Support Vector Machine (SVM) classifier identifies up to eighteen mobile activities and a four-directional foot control gesture at approximately 98% accuracy. By understanding user’s present activities and foot gestures, this system offers a nonintrusive and always-available input method. We present the design and implementation of our system and several proof-of-concept applications.
Overview:
A person’s weight is not allocated symmetrically over the plantar. As the sole is not flat but arched, the weight mainly centers on the hallex, the first metatarse and the calcaneus. When sitting, the weight of a person’s upper body rest mostly on the chair and the weight on the feet is relatively small. When standing, the whole body’s weight is put evenly on both feet. Leaning left or right changes the weight distribution over the feet. When walking, the weight distribution changes with the pace; the weight on the front and rear part of the foot alternately increases and decreases because not all parts of the sole contact the ground at once. The changes in weight distribution on the feet reflect one’s activity, and different activities have different changes of weight distribution signatures.
[pic]
We observed people’s common low-level activities in a mobile context and classified them as static or dynamic. Static activities include sitting (with variations: sitting straight, stretching out and legs shaking) and standing (with variations: standing straight, leaning to left and right, swinging and slouching); for dynamic categories, we want to know walking (slow and fast,
Abstract:
Sensible Shoes is a hands-free and eyes-free foot-computer interface that supports on-the-go interaction with surrounding environments. We recognize different low-level activities by measuring the user’s continuous weight distribution over the feet with twelve Force Sensing Resistor (FSR) sensors embedded in the insoles of shoes. Using the sensor data as inputs, a Support Vector Machine (SVM) classifier identifies up to eighteen mobile activities and a four-directional foot control gesture at approximately 98% accuracy. By understanding user’s present activities and foot gestures, this system offers a nonintrusive and always-available input method. We present the design and implementation of our system and several proof-of-concept applications.
Overview:
A person’s weight is not allocated symmetrically over the plantar. As the sole is not flat but arched, the weight mainly centers on the hallex, the first metatarse and the calcaneus. When sitting, the weight of a person’s upper body rest mostly on the chair and the weight on the feet is relatively small. When standing, the whole body’s weight is put evenly on both feet. Leaning left or right changes the weight distribution over the feet. When walking, the weight distribution changes with the pace; the weight on the front and rear part of the foot alternately increases and decreases because not all parts of the sole contact the ground at once. The changes in weight distribution on the feet reflect one’s activity, and different activities have different changes of weight distribution signatures.
[pic]
We observed people’s common low-level activities in a mobile context and classified them as static or dynamic. Static activities include sitting (with variations: sitting straight, stretching out and legs shaking) and standing (with variations: standing straight, leaning to left and right, swinging and slouching); for dynamic categories, we want to know walking (slow and fast,