Contribution of various forefoot areas to push-off peak at different speeds and slopes during walking.

BACKGROUND: Push-off during the terminal stance phase has a major impact on forward progression during walking. During this phase, the ground reaction force is applied to a small area under the forefoot. A better understanding of how single forefoot areas contribute to push-off peak in healthy subjects is needed to develop biomimetic orthopedic devices for forefoot amputees. RESEARCH QUESTION: What is the contribution of different forefoot sole areas to push-off peak as a function of speed and slope? METHODS: In this analytical study, 15 healthy subjects walked on a treadmill at different speeds (0.8 m/s; 1.2 m/s; 1.6 m/s; max. gait speed) without de-/inclination and on different slopes (-10°; -5°; 0°; 5°; 10°) with normal walking speed. The Novel Pedar-X System was used to measure vertical sole force. Push-off peak of the entire sole was determined and relative contributions of the areas under the hallux, first ray, and toes (I-V) were calculated and analyzed using separate repeated-measures ANOVA (α = 0.05). RESULTS: Push-off peak increases with faster walking speeds as well as with 10° inclination. Downhill walking is associated with a reduced push-off peak. The contribution of all forefoot areas increases with faster walking speeds and at a declination of -10°. Push-off contribution of the area under the hallux increases by about 64.6% at fast walking compared to slow walking and this increase is higher than that of the area under the first ray and toes (p < 0.05). SIGNIFICANCE: These findings indicate the major role of the hallux in speed generation and the importance of the forefoot during downhill walking. The results show the need for an adequate assistive device even in hallux amputation cases to compensate for deficits in the push-off phase.

Workplace activity classification from shoe-based movement sensors.

BACKGROUND: High occupational physical activity is associated with lower health. Shoe-based movement sensors can provide an objective measurement of occupational physical activity in a lab setting but the performance of such methods in a free-living environment have not been investigated. The aim of the current study was to investigate the feasibility and accuracy of shoe sensor-based activity classification in an industrial work setting. RESULTS: An initial calibration part was performed with 35 subjects who performed different workplace activities in a structured lab setting while the movement was measured by a shoe-sensor. Three different machine-learning models (random forest (RF), support vector machine and k-nearest neighbour) were trained to classify activities using the collected lab data. In a second validation part, 29 industry workers were followed at work while an observer noted their activities and the movement was captured with a shoe-based movement sensor. The performance of the trained classification models were validated using the free-living workplace data. The RF classifier consistently outperformed the other models with a substantial difference in in the free-living validation. The accuracy of the initial RF classifier was 83% in the lab setting and 43% in the free-living validation. After combining activities that was difficult to discriminate the accuracy increased to 96 and 71% in the lab and free-living setting respectively. In the free-living part, 99% of the collected samples either consisted of stationary activities or walking. CONCLUSIONS: Walking and stationary activities can be classified with high accuracy from a shoe-based movement sensor in a free-living occupational setting. The distribution of activities at the workplace should be considered when validating activity classification models in a free-living setting.