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Digital tracking of human motion offers the potential to monitor a wide range of activities detecting normal versus abnormal performance of tasks. We examined the ability of a wearable, conformal sensor system, fabricated from stretchable electronics with contained accelerometers and gyroscopes, to specifically detect, monitor, and define motion signals and “signatures,” associated with tasks of daily living activities. The sensor system was affixed to the dominant hand of healthy volunteers (n = 4) who then completed four tasks. For all tasks examined, motion data could be captured, monitored continuously, uploaded to the digital cloud, and stored for further analysis. Acceleration and gyroscope data were collected in the x-, y-, and z-axes, yielding unique patterns of component motion signals for each task studied. Upon analysis, low-frequency (<10 Hz) tasks (walking, drinking from a mug, and opening a pill bottle) showed low intersubject variability (<0.3g difference) and low interrepetition variability (<0.1g difference) when comparing the acceleration of each axis for a single task. High-frequency (≥10 Hz) activity (brushing teeth) yielded low intersubject variability of peak frequencies in acceleration of each axis. Each motion task was readily distinguishable and identifiable (with ≥70% accuracy) by independent observers from motion signatures alone, without the need for direct visual observation. Stretchable electronic technologies offer the potential to provide wireless capture, tracking, and analysis of detailed directional components of motion for a wide range of individual activities and functional status.