Objective: The goal of this article is to present and evaluate a sensor-based falling risk estimation system. The system consists of an array of Wii Balance Boards (WBB) and an exergame that estimates if the player is at an increased falling risk by predicting the result of the 30 Second Chair-Stand Test (30CST).
Methods: 16 participants recruited at a nursing home performed the 30CST and then played the exergame as often as desired during a period of two weeks. For each session, features related to how they walk and stand on the WBBs while playing the exergame were collected. Different classifier algorithms were used to predict the result of the 30CST on a binary basis (able or unable to maintain physical independence).
Results: We achieved a maximum accuracy of 91% when attempting to estimate if the player’s 30CST score will be over or under a threshold of 12 points using a Logistic Model Tree. We also believe it is feasible to predict age- and sex-adjusted cutoff scores.
Conclusion: An array of WBBs seems to be a viable solution to estimate lower extremity strength and with it the falling risk. In addition, data extracted while playing may form a basis to perform a general screening to identify elderly at an increased falling risk.