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Movement data from athletes are useful to quantify performance or more specifically the workload. Inertial measurement units (IMUs) are useful sensors to quantify body movements. Sensor placement on human body is still an open question that this paper focuses on. A method that develops synthesized inertial data is proposed for determining optimal sensors placement. Comparison between virtual and real inertial data is achieved. Training motion recognition algorithm on synthesized and real inertial data exhibits less than 7% difference. This method highlights the ability of the numerical model to determine relevant sensor placement of IMUs on human body for motion recognition algorithm using virtual sensors.

Physical activity involves movements, which can be considered sources of kinetic energy, that are expected to be important during sports activities. Several transducers can transform this energy into electrical energy. Piezoelectric generators are widely used, and several applications highlight their relevance. However, the generated output power is location dependent, and the analysis of the placement of this kind of generator can be challenging. In order to assess the availability of kinetic energy sources, an acceleration data analysis method is presented. Temporal and harvester model-based studies, using data from 17 inertial measurement units (IMUs) located across the whole human body, were conducted. The results show that piezoelectric cantilever-beam harvesters can be very sensitive to impacts. Extremity segments, such as the feet or hands, can be considered as good energy sources. The most relevant features are proposed as criteria to easily evaluate the harvestable energy sources.

Smart workwear systems with embedded inertial measurement unit sensors are developed for convenient ergonomic risk assessment of occupational activities. However, its measurement accuracy can be affected by potential cloth artifacts, which have not been previously assessed. Therefore, it is crucial to evaluate the accuracy of sensors placed in the workwear systems for research and practice purposes. This study aimed to compare in-cloth and on-skin sensors for assessing upper arms and trunk postures and movements, with the on-skin sensors as the reference. Five simulated work tasks were performed by twelve subjects (seven women and five men). Results showed that the mean (±SD) absolute cloth–skin sensor differences of the median dominant arm elevation angle ranged between 1.2° (±1.4) and 4.1° (±3.5). For the median trunk flexion angle, the mean absolute cloth–skin sensor differences ranged between 2.7° (±1.7) and 3.7° (±3.9). Larger errors were observed for the 90th and 95th percentiles of inclination angles and inclination velocities. The performance depended on the tasks and was affected by individual factors, such as the fit of the clothes. Potential error compensation algorithms need to be investigated in future work. In conclusion, in-cloth sensors showed acceptable accuracy for measuring upper arm and trunk postures and movements on a group level. Considering the balance of accuracy, comfort, and usability, such a system can potentially be a practical tool for ergonomic assessment for researchers and practitioners.