Toward community-based wheelchair evaluation with machine learning methods

Abstract

Introduction: Upper extremity pain among manual wheelchair users induces functional decline and reduces quality of life. Research has identified chronic overuse due to wheelchair propulsion as one of the factors associated with upper limb injuries. Lack of a feasible tool to track wheelchair propulsion in the community precludes testing validity of wheelchair propulsion performed in the laboratory. Recent studies have shown that wheelchair propulsion can be tracked through machine learning methods and wearable accelerometers. Better results were found in subject-specific machine learning method. To further develop this technique, we conducted a pilot study examining the feasibility of measuring wheelchair propulsion patterns.

Methods: Two participants, an experienced manual wheelchair user and an able-bodied individual, wore two accelerometers on their arms. The manual wheelchair user performed wheelchair propulsion patterns on a wheelchair roller system and overground. The able-bodied participant performed common daily activities such as cooking, cleaning, and eating.

Results: The support vector machine built from the wrist and arm acceleration of wheelchair propulsion pattern recorded on the wheelchair roller system predicted the wheelchair propulsion patterns performed overground with 99.7% accuracy. The support vector machine built from additional rotation data recorded overground predicted wheelchair propulsion patterns (F1 = 0.968).

Conclusions: These results further demonstrate the possibility of tracking wheelchair propulsion in the community.

Keywords: Machine learning; accelerometer; inertial measurement unit; kinematic; outcome measure; wearable sensors; wheelchair propulsion.

Figure 1.

The WheelMill System.

Figure 2.

Three-second epoch window normalized confusion matrix for (a) accelerometer and (b) IMU.

Figure 3.

Two-second epoch window normalized confusion matrix for (a) accelerometer and (b) IMU.

Figure 4.

F1 score comparison between different sensors and different epoch windows.