BACKGROUND: Transfers are an important skill for many wheelchair users. However, they have also been related to the risk of falling or developing upper limb injuries.
Transfer abilities are usually evaluated in clinical settings or biomechanics laboratories and these methods of assessment are poorly suited to evaluation in real
and unconstrained world settings where transfers take place.
OBJECTIVE: The objective of this paper is to develop a strategy to enable transfer quality evaluation and improve the predictive accuracy of transfer detection using a single wearable low cost accelerometer.
METHODS: We collected data from nine wheelchair users wearing tri-axial accelerometer on their chest while performing transfers to and from car seats and
home furniture. We then extracted significant features from accelerometer data based on biomechanical considerations and previous relevant literature and used
machine learning algorithms to evaluate the performance of wheelchair transfers and detect their occurrence from a continuous time series of data.
RESULTS: Results show that the best predictive accuracy for Automatic Transfer Quality Evaluation was obtained with Support Vector Machine (SVM) classifiers when determining use of head-hip relationship (75.93%) and smoothness of landing
(79.62%), when the start and end of the transfer are known. Automatic Transfer
Detection reaches an accuracy of 87.8% using Multinomial Logistic Regression (MLR) classifiers, which is in line with the state of the art in this context. However,
we achieve these results using only a single sensor and collecting data in a more ecological manner.
CONCLUSIONS: The use of a single chest-placed accelerometer shows a predictive accuracy of over 75% for algorithms applied independently to both transfer evaluation and monitoring. This points to the opportunity for designing ubiquitous technology for personalized skill development interventions targeting wheelchair
users. However, monitoring transfers still requires the use of external inputs or extra sensors to identify start and end of the transfer, which are needed to perform an
accurate evaluation
