Journal: Advanced Journal of Nursing DOI: 10.32629/ajn.v7i3.5565
Abstract
In recent years, wearable sensors have opened the door to posture tracking outside the clinic. Inertial measurement units (IMUs), often combined with pressure, electromyographic, or flexible strain sensors, provide continuous information about body position and the effort of the muscles that hold it. Machine learning turns these raw signals into recognizable activity and posture states, and real-time feedback can prompt users to correct themselves as they go about daily life. This review brings together recent work on multimodal sensing, human activity recognition, and closed-loop intervention, with particular attention to spinal health. We also look closely at the obstacles that keep recurring in the literature: sensors drift and shift on the body, algorithms trained on one group of users often fail on another, and there is still no shared definition of what counts as an abnormal posture. Feedback fatigue, the tendency to ignore alerts after continued exposure, further limits long-term acceptance. We argue that progress will depend on personalized calibration, feedback that adapts to the individual user, and assessment that reaches beyond short experiments. In our view, wearable posture devices are moving toward integrated spinal health management rather than serving as simple reminders.
Keywords
wearable sensors; posture monitoring; IMU; multimodal fusion; activity recognition; intelligent feedback; spinal health
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