Evaluation of Different Pressure-Based Foot Contact Event Detection Algorithms across Different Slopes and Speeds

If validated, in-shoe pressure measuring technology allows for the field-based quantification of running gait, including kinematic and kinetic measures. Different algorithmic methods have been proposed to determine foot contact events from in-shoe pressure insole systems, however, these methods have not been evaluated for accuracy, reliability against a gold standard using running data across different slopes, and speeds. Using data from a plantar pressure measurement system, seven different foot contact event detection algorithms based on pressure signals (pressure sum) were compared to vertical ground reaction force data collected from a force instrumented treadmill. Subjects ran on level ground at 2.6, 3.0, 3.4, and 3.8 m/s, six degrees (10.5%) inclined at 2.6, 2.8, and 3.0 m/s, and six degrees declined at 2.6, 2.8, 3.0, and 3.4 m/s. The best performing foot contact event detection algorithm showed maximal mean absolute errors of only 1.0 ms and 5.2 ms for foot contact and foot off, respectively, on level grade, when compared to a 40 N ascending and descending force threshold from the force treadmill data. Additionally, this algorithm was unaffected by grade and had similar levels of errors across all grades.

Smart Insole Based on Flexi Force and Flex Sensor for Monitoring Different Body Postures

This study aims to fabricate smart insoles using wireless Flexi force and bend sensing technology. Polyvinyl chloride (PVC) film was chosen as the substrate to hold all the sensors. The developed smart insole has a three-layer structure (insole-PVC layer-fabric layer) and is calibrated in an isolation laboratory to evaluate its measurement performance. One male volunteer subject exhibited four different body postures, namely tree pose, forward-leaning, squatting, and forward folding pose. Changes in pressure distribution were considered to be similar for the forward, squat, and forward-folded positions. When subjects performed a full squat, the flex sensor exhibited maximum flexion during the squat position, and the flex sensor response against the squat pose was found to be higher by about 18.18% than in the forward lean, respectively. The tree pose has the highest error rate at the first metatarsal, about 18.6%, of which the maximum absolute relative error of the sensor is less than 5%. Plantar pressure distribution and body posture measurements were successfully validated using Flexi force and flex sensors embedded in the smart insole. The smart insole proposed in this research work has broader prospects for clinical application.

StressFoot: Uncovering the Potential of the Foot for Acute Stress Sensing in Sitting Posture

Stress is a naturally occurring psychological response and identifiable by several body signs. We propose a novel way to discriminate acute stress and relaxation, using movement and posture characteristics of the foot. Based on data collected from 23 participants performing tasks that induced stress and relaxation, we developed several machine learning models to construct the validity of our method. We tested our models in another study with 11 additional participants. The results demonstrated replicability with an overall accuracy of 87%. To also demonstrate external validity, we conducted a field study with 10 participants, performing their usual everyday office tasks over a working day. The results showed substantial robustness. We describe ten significant features in detail to enable an easy replication of our models.