From muscles to motion: the role of sensor layout and physiological factors in hand motion decoding.

Despite substantial progress in decoding biosignals for human motion prediction, the influence of participant- and experiment-related factors on the decodability of these signals has received comparatively little attention. This study evaluates the continuous prediction of hand and wrist joint flexion using the MyoKi database, which comprises surface electromyography, inertial measurement units, and force myography data from 35 participants without disabilities performing 74 daily-life tasks. Unlike existing datasets, MyoKi includes tasks that mimic real-world scenarios by allowing natural movement variations and muscle fatigue. Using a long short-term memory neural network, the impact of participant- and experiment-related factors on decoding accuracy was investigated. Our results show that both expanding sensor coverage to additional muscle regions and combining multiple sensor modalities significantly improve decoding performance, with the greatest gains observed for joints controlled by extrinsic muscles. Muscle fatigue, recording time, and participant characteristics such as weight also influenced model accuracy. However, decoding of movements driven by intrinsic hand muscles remains challenging due to anatomical limitations. These findings highlight the importance of sensor placement and multimodal fusion for myoelectric decoding and provide guidance for optimizing sensor configurations in future prosthetic and robotic applications.