Detecting destabilizing wheelchair conditions for maintaining seated posture

The purpose of this study was to detect and classify potentially destabilizing conditions encountered by manual wheelchair users with spinal cord injuries (SCI) to dynamically increase stability and prevent falls.

A volunteer with motor complete T11 paraplegia repeatedly propelled his manual wheelchair over level ground and simulated destabilizing conditions including sudden stops, bumps and rough terrain. Wireless inertial measurement units attached to the wheelchair frame and his sternum recorded associated accelerations and angular velocities. Algorithms based on mean, standard deviation and minimum Mahalanobis distance between conditions were constructed and applied to the data off-line to discriminate between events. Classification accuracy was computed to assess effects of sensor position and potential for automatically selecting a dynamic intervention to best stabilize the wheelchair user.

The decision algorithm based on acceleration signals successfully differentiated destabilizing conditions and level over-ground propulsion with classification accuracies of 95.8, 58.3 and 91.7% for the chest, wheelchair and both sensors, respectively.

Mahalanobis distance classification based on trunk accelerations is a feasible method for detecting destabilizing events encountered by wheelchair users and may serve as an effective trigger for protective interventions. Incorporating data from wheelchair-mounted sensors decreases the false negative rate. Implications for Rehabilitation SCI has a significant impact on quality of life, compromising the ability to participate in social or leisure activities, and complete other activities of daily living for an independent lifestyle. Using inertial measurement units to build an event classifier for control the actions of a neuroprosthetic device for maintaining seated posture in wheelchair users. Varying muscle activation increases user stability reducing the risk of injury.