Research on Intelligent Wheelchair Attitude-Based Adjustment Method Based on Action Intention Recognition

Abstract

At present, research on intelligent wheelchairs mostly focuses on motion control, while research on attitude-based adjustment is relatively insufficient. The existing methods for adjusting wheelchair posture generally lack collaborative control and good human–machine collaboration. This article proposes an intelligent wheelchair posture-adjustment method based on action intention recognition by studying the relationship between the force changes on the contact surface between the human body and the wheelchair and the action intention. This method is applied to a multi-part adjustable electric wheelchair, which is equipped with multiple force sensors to collect pressure information from various parts of the passenger’s body. The upper level of the system converts the pressure data into the form of a pressure distribution map, extracts the shape features using the VIT deep learning model, identifies and classifies them, and ultimately identifies the action intentions of the passengers. Based on different action intentions, the electric actuator is controlled to adjust the wheelchair posture. After testing, this method can effectively collect the body pressure data of passengers, with an accuracy of over 95% for the three common intentions of lying down, sitting up, and standing up. The wheelchair can adjust its posture based on the recognition results. By adjusting the wheelchair posture through this method, users do not need to wear additional equipment and are less affected by the external environment. The target function can be achieved with simple learning, which has good human–machine collaboration and can solve the problem of some people having difficulty adjusting the wheelchair posture independently during wheelchair use.

Improving wheelchair user sitting posture to alleviate lumbar fatigue: a study utilizing sEMG and pressure sensors

“…It can better restore the force of the back of the wheelchair and the cushion, and use it to distinguish the sitting situation of the human body. According to the actual sampling requirements, our team independently designed a collection card system (Cui et al, 2023), which can collect and report data for each point.…”

A modular, deep learning-based holistic intent sensing system tested with Parkinson’s disease patients and controls

“…This paper proposes that intent sensing could be a useful input for control of assistive devices to help those living with Parkinson's disease maintain their quality of life. It has been shown in previous studies to be an effective tool for the control of upper limb orthoses which assist motion (7), and provide intelligent attitude-adjustment for smart wheelchairs (8). These technologies can be applied for the support of patients with Parkinson's disease, highlighting the potential developing intent technology for helping those with this condition.…”

Research on Intelligent Wheelchair Multimode Human–Computer Interaction and Assisted Driving Technology

“…In terms of human-computer interaction between the occupant and the wheelchair, Xu et al designed an intelligent wheelchair with eye-movement control, which acquires the occupant's eye image through a camera and uses deep learning to determine the direction of eye movement, as well as to establish a motion acceleration model for the intelligent wheelchair to improve motion smoothness [15]. Cui et al proposed an intelligent wheelchair posture adjustment method based on action intent recognition, which adjusts the wheelchair posture by investigating the relationship between the force changes on the contact surface between the human body and the wheelchair and the action intent [16]. Wanluk proposed the concept of an eye-tracking intelligent wheelchair, which can control the movement of the wheelchair through the eyes and remotely control some electrical devices [17].…”