Abstract
Powered wheelchairs have enhanced the mobility and quality of life of people with special needs. The next step in the development of powered wheelchairs is to incorporate sensors and electronic systems for new control applications and capabilities to improve their usability and the safety of their operation, such as obstacle avoidance or autonomous driving. However, autonomous powered wheelchairs require safe navigation in different environments and scenarios, making their development complex. In our research, we propose, instead, to develop contactless control for powered wheelchairs where the position of the caregiver is used as a control reference. Hence, we used a depth camera to recognize the caregiver and measure at the same time their relative distance from the powered wheelchair. In this paper, we compared two different approaches for real-time object recognition using a 3DHOG hand-crafted object descriptor based on a 3D extension of the histogram of oriented gradients (HOG) and a convolutional neural network based on YOLOv4-Tiny. To evaluate both approaches, we constructed Miun-Feet—a custom dataset of images of labeled caregiver’s feet in different scenarios, with backgrounds, objects, and lighting conditions. The experimental results showed that the YOLOv4-Tiny approach outperformed 3DHOG in all the analyzed cases. In addition, the results showed that the recognition accuracy was not improved using the depth channel, enabling the use of a monocular RGB camera only instead of a depth camera and reducing the computational cost and heat dissipation limitations. Hence, the paper proposes an additional method to compute the caregiver’s distance and angle from the Powered Wheelchair (PW) using only the RGB data. This work shows that it is feasible to use the location of the caregiver’s feet as a control signal for the control of a powered wheelchair and that it is possible to use a monocular RGB camera to compute their relative positions.
Highlights
Powered wheelchairs (PWs) have improved the quality of life of many disabled people by providing them more independence and greater transport means, reducing their dependence on caregivers
This work is focused on the development of a semi-autonomous contactless control of PWs that uses the position of a caregiver as a reference control in a side-by-side configuration
We evaluated a hand-crafted approach based on the 3DHOG in terms of Mean Average Precision (mAP)
Summary
Powered wheelchairs (PWs) have improved the quality of life of many disabled people by providing them more independence and greater transport means, reducing their dependence on caregivers. The step in PWs’ development is to improve their usability and the safety of their operation by integrating new features such as obstacle avoidance or autonomous driving control. This involves the use of additional sensors and electronic systems to measure the PW’s environment, detect objects, and determine their relative positions. Contactless control improves the PW’s operation when the user cannot properly control the PW, as well as improves the communication between the caregiver and the PW user, and the user’s quality of life [1].
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