For over two centuries, the wheelchair has been one of the most common assistive devices for individuals with locomotor impairments without much modifications. Wheelchair control is a complex motor task that increases both the physical and cognitive workload. New wheelchair interfaces, including Power Assisted devices, can further augment users by reducing the required physical effort, however little is known on the mental effort implications. In this study, we adopted a neuroergonomic approach utilizing mobile and wireless functional near infrared spectroscopy (fNIRS) based brain monitoring of physically active participants. 48 volunteers (30 novice and 18 experienced) selfpropelled on a wheelchair with and without a PowerAssist interface in both simple and complex realistic environments. Results indicated that as expected, the complex, more difficult environment led to lower task performance complemented by higher prefrontal cortex activity compared to the simple environment. The use of the PowerAssist feature had significantly lower brain activation compared to traditional manual control only for novices. Expertise led to a lower brain activation pattern within the middle frontal gyrus, complemented by performance metrics that involve lower cognitive workload. Results here confirm the potential of the Neuroergonomic approach and that direct neural activity measures can complement and enhance task performance metrics. We conclude that the cognitive workload benefits of PowerAssist are more directed to new users and difficult settings. The approach demonstrated here can be utilized in future studies to enable greater personalization and understanding of mobility interfaces within real-world dynamic environments.
Measuring manual wheelchair activity by using wearable sensors is on the rise for rehabilitation and monitoring purposes. Stroke pattern is an important descriptor of the wheelchair user's quality of movement. This paper evaluates the capability of inertial sensors located at different upper limb locations plus wheel, to classify two types of stroke pattern for manual wheelchairs: semicircle and arc. Data was collected using bespoke inertial sensors with a wheelchair fixed to a treadmill. Classification was done with a linear SVM algorithm, and classification performance was computed for each sensor location in the upper limb, and then in combination with wheel sensor. For single sensors, forearm location had the highest accuracy (96%) followed by hand (93%) and arm (90%). For combined sensor location with wheel, best accuracy came in combination with forearm. These results set the direction towards a wearable wheelchair monitor that can offer multiple on-body locations for increased usability.
Manual wheelchair users experience numerous invisible barriers while navigating cities, often reporting how stressful journeys are. This stress affects a wheelchair user's quality of life. To alleviate such psychological burden, we propose a novel intervention strategy with a respiratory biofeedback interface which is designed to help users feel relaxed in urban navigation. We conduct a study in a real-world setting to explore its potential to provide real-time psychological support. From qualitative and quantitative analysis, we report on strengths and weaknesses of the approach.
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