Mobility profile and wheelchair driving skills of powered wheelchair users: sensor-based event recognition using a support vector machine classifier

Skills assessment metrics of electric powered wheelchair driving in a virtual environment: a survey

The purpose of this review is to present studies on the parameters for assessing the skills of users of electric wheelchair driving simulators in a virtual environment. In addition, this study also aims to identify the most widely used and validated parameters for the quantification of electric wheelchair driving ability in a virtual environment and to suggest challenges for future research. To carry out this research, the criteria of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) were adopted. Literature searches in English, French, and Portuguese were conducted up to December 2020 in the PubMed, SciELO, Science Direct, World Wide Science, and Scopus databases. The keywords used were electric wheelchair, simulator, performance indicators, performance skills, driving skills, training platform, virtual environment, and virtual reality. We excluded studies involving "real" wheelchairs without a simulator in a virtual environment. We have selected a total of 42 items. In these studies, we identified 32 parameters (3 qualitative and 29 quantitative) that are used as parameters for the evaluation of the ability to control a powered wheelchair in a virtual environment. Although the amount of research in this area has increased significantly in recent years, additional studies are still needed to provide a more accurate and objective assessment of skills among the target population. A challenge for future work is the increasing application of artificial intelligence techniques and the exploration of biomedical data measurements, which may be a promising alternative to improve the quantification of user competencies.

SenseJoy, a pluggable solution for assessing user behavior during powered wheelchair driving tasks

BACKGROUND

The complex task of Electric Powered Wheelchairs (EPW) prescription relies mainly on personal experience and subjective observations despite standardized processes and protocols. The most informative measurements come from joystick monitoring, but recording direct joystick outputs require to disassemble the joystick. We propose a new solution called "SenseJoy" that is easy to plug on a joystick and is suitable to characterize the driver behavior by estimating the joystick command.

METHODS

SenseJoy is a pluggable system embedded on EPW built with a 3D accelerometer and a 2D gyrometer placed within the joystick and another 3D accelerometer located at the basis of the joystick. Data is sampled at 39 Hz and processed offline. First, SenseJoy sensitivity is assessed on wheelchair driving tasks performed by a group of 8 drivers (31 ± 8 years old, including one driver with left hemiplegia, one with cerebral palsy) in a lab environment. Direct joystick measurements are compared with SenseJoy estimations in different driving exercises. A second group of 5 drivers is recorded in the ecological context of a rehabilitation center (41 ± 10 years old, with two tetraplegic drivers, one tetraplegic driver with cognitive disorder, one driver post-stroke, one driver with right hemiplegia). The measurements from all groups of drivers are evaluated with an unsupervised statistical analysis, to estimate driving profile clusters.

RESULTS

The SenseJoy is able to measure the EPW joystick inclination angles with a resolution of 1.31% and 1.23% in backward/forward and left/right directions respectively. A statistical validation ensures that the classical joystick-based indicators are equivalent when acquired with the SenseJoy or with a direct joystick output connection. Using an unsupervised methodology, based on a similarity matrix between subjects, it is possible to characterize the driver profile from real data.

CONCLUSION

SenseJoy is a pluggable system for assessing the joystick controls during EPW driving tasks. This system can be plugged on any EPW equipped with a joystick control interface. We demonstrate that it correctly estimates the performance indicators and it is able to characterize driving profile. The system is suitable and efficient to assist therapists in their recommendation, by providing objective measures with a fast installation process.

Automatic Detection and Classification of Unsafe Events During Power Wheelchair Use

Using a powered wheelchair (PW) is a complex task requiring advanced perceptual and motor control skills. Unfortunately, PW incidents and accidents are not uncommon and their consequences can be serious. The objective of this paper is to develop technological tools that can be used to characterize a wheelchair user’s driving behavior under various settings. In the experiments conducted, PWs are outfitted with a datalogging platform that records, in real-time, the 3-D acceleration of the PW. Data collection was conducted over 35 different activities, designed to capture a spectrum of PW driving events performed at different speeds (collisions with fixed or moving objects, rolling on incline plane, and rolling across multiple types obstacles). The data was processed using time-series analysis and data mining techniques, to automatically detect and identify the different events. We compared the classification accuracy using four different types of time-series features: 1) time-delay embeddings; 2) time-domain characterization; 3) frequency-domain features; and 4) wavelet transforms. In the analysis, we compared the classification accuracy obtained when distinguishing between safe and unsafe events during each of the 35 different activities. For the purposes of this study, unsafe events were defined as activities containing collisions against objects at different speed, and the remainder were defined as safe events. We were able to accurately detect 98% of unsafe events, with a low (12%) false positive rate, using only five examples of each activity. This proof-of-concept study shows that the proposed approach has the potential of capturing, based on limited input from embedded sensors, contextual information on PW use, and of automatically characterizing a user’s PW driving behavior.