  How to design an optimal wearable device for human movement recognition is
vital to reliable and accurate human-machine collaboration. Previous works
mainly fabricate wearable devices heuristically. Instead, this paper raises an
academic question: can we design an optimization algorithm to optimize the
fabrication of wearable devices such as figuring out the best sensor
arrangement automatically? Specifically, this work focuses on optimizing the
placement of Forcemyography (FMG) sensors for FMG armbands in the application
of arm movement recognition. Firstly, based on graph theory, the armband is
modeled considering sensors' signals and connectivity. Then, a Graph-based
Armband Modeling Network (GAM-Net) is introduced for arm movement recognition.
Afterward, the sensor placement optimization for FMG armbands is formulated and
an optimization algorithm with greedy local search is proposed. To study the
effectiveness of our optimization algorithm, a dataset for mechanical
maintenance tasks using FMG armbands with 16 sensors is collected. Our
experiments show that using only 4 sensors optimized with our algorithm can
help maintain a comparable recognition accuracy to using all sensors. Finally,
the optimized sensor placement result is verified from a physiological view.
This work would like to shed light on the automatic fabrication of wearable
devices considering downstream tasks, such as human biological signal
collection and movement recognition. Our code and dataset are available at
https://github.com/JerryX1110/IROS22-FMG-Sensor-Optimization
