GMAC—A Simple Measure to Quantify Upper Limb Use From Wrist-Worn Accelerometers

Various measures have been proposed to quantify upper-limb use through wrist-worn inertial measurement units. The two most popular traditional measures of upper-limb use – thresholded activity counts (TAC) and the gross movement (GM) score suffer from high sensitivity and low specificity, and vice versa. We previously proposed a hybrid version of these two measures – the GMAC – that showed better overall detection performance than TAC and GM. In this paper, we answer two critical questions to improve the GMAC measure’s usefulness: (a) can it be implemented using only the accelerometer data? (b) what are its optimal parameter values? Here, we propose a modified GMAC using only the accelerometer data and optimize its parameters to develop: (a) a generic measure that is both limb- and subject-independent, and (b) limb-specific measures that were only subject-independent. The optimized GMAC showed better detection performance than the previous GMAC and surprisingly had comparable performance to the best-performing machine learning-based measure (random forest inter-subject model). In hemiparetic data, its performance was similar to the previous GMAC and the random forest inter-subject model; the limb-specific GMAC measure, however, had a better performance than the generic measure. The optimized limb-specific GMAC is a simple, interpretable alternative to a machine learning-based inter-subject model. The optimized GMAC can be a valuable measure for offline or real-time detection and feedback of upper limb use. The preliminary results of this study, based on a small dataset, need to be validated on a larger dataset to evaluate its generalizability.