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This paper describes a canine posture detection system composed of wearable sensors and instrumented devices that detect the postures sit, stand, and eat.
In the Zigbee sensor network, each posture detection module can communicate with the robot onboard computer.
The complete algorithm has been realized in the microcontroller of the human posture detection module.
The human posture detection is based on a triaxial accelerometer and the developed posture recognition algorithms.
Further, the posture detection module has a Zigbee chip and an 8-bit microcontroller on board.
The impact of having a faulty sensor management is also tested in the real-life experiment for posture detection.
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The sensor drift in this application should be held reasonably low, as errors in position detection multiply and prediction of current posture gets worse over time.
We design and conduct experiments of posture estimation, fingertip detection, character recognition and localisation.
The proposed descriptor serves both posture recognition and fingertip detection.
Grammar-Based, Posture- and Context-Cognitive Detection for Falls with Different Activity Levels.
At the same time, dynamic strain sensing assisted by signal processing system provides possibility of using such DSSG for real-time elbow joint motion detection and posture analysis, thereby indicating its great potentials for human-machine interface, prosthetic, and biomechanical applications.
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