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Intent sensing is a growing field within medical device control, with major potential applications for assistive devices, such as prosthetics. As many sensors as possible should be utilised to maximise accuracy. The availability of sensors may change over time due to changing surroundings or activities, sensors failing, and electrode contact being lost. The sensor network should be dynamic and modular in nature, continuing to function even when some sensors are unavailable. The management of sensor unavailability may help to reduce the need for device maintenance, particularly in developing nations with limited availability of these services. An algorithm is proposed to classify intent using networked sensors in real time. Data are gathered using human participants wearing four surface electromyography sensors and performing a pseudo-random sequence of grasps. The relationship between time offset and prediction accuracy is investigated, with the algorithm predicting future intent actions up to half a second in advance. Sensor dropout is simulated by randomly replacing sensor readings with recorded noise. The new algorithm is compared to existing algorithms and shown to be more accurate in situations of sensor dropout, with the difference increasing as more sensors become unavailable. This suggests that when reductions in sensing capabilities are likely to occur over time, the modular method is more appropriate for control.

Original publication




Journal article





Publication Date





453 - 466