Abstract/Summary

There is a growing interest in measuring human activities via worn inertial sensors, and situating two accelerometers on a body segment allows accessing rotational kinematic information, at a significantly lower energy cost when compared to gyroscopes. However, the placement of sensors has not been widely considered in the literature. In practice, dual-accelerometer systems should be built as compact as possible to ensure long-term wearability. In this study, the impact of sensor placement and nature of human activity on signal quality is quantified by individual and differential Signal to Noise Ratios (SNR). To do so, noise-free signals are described by a 2D kinematic model of a body segment as a function of kinematic variables and sensor location on the segment. Measurements are modelled as kinematic signals disturbed by zero mean additive Gaussian noise. Depending on the accuracy needed, one can choose a minimal SNR to achieve, with such dual-accelerometer arrangement. We estimate SNR and minimal sensor separations for three datasets, two from the public domain and one collected for this paper. The datasets give arm motion profiles for reaching, inertial data collected during locomotion on a treadmill and during activities of daily life. With a dual-accelerometer arrangement, we show it is possible to achieve a good differential SNR for the analysis of various human activities if the separation between the two sensors and their placement are well chosen.