Elbow Joint Angle Estimation Using a Low-Cost and Low-Power Single Inertial Device for Daily Home-Based Self-Rehabilitation - Manon Fourniol; Rémy Vauché; Guillaume Rao; Eric Watelain; Edith Kussener (Journal of Low Power Electronics and Applications)
In the context of aging populations, it has become necessary to develop new methods and devices for the daily home-based self-rehabilitation of elderly people. To this end, this paper proposes and evaluates the use of an easy-to-use single battery-powered device including a 3D accelerometer and a 3D gyroscope, where light algorithms, such as the complementary filter and the Kalman filter, are implemented to estimate the elbow joint angle. During experiments, a robotic arm and a human arm were used to obtain an error interval for each tested algorithm; the robotic arm allows for reproducible movements and reproducible results, which allows us to independently verify the impact of parameters such as the sensor’s movement speed on the algorithm precision. The experimental results show that the algorithm that uses only accelerometer data is one of the most relevant since it allows us to obtain a Root Mean Square Error between 1.83° and 5.52° at a sensor data rate of 100 Hz, which is similar to the results obtained using the data fusion algorithms tested. Nevertheless, it has a lower power consumption since it requires only 58 cycles when using an ARM Cortex-M4 processor (which is lower than that of the other data fusion algorithms tested by a factor of at least two), and it does not necessitate the additional sensor required by the other data fusion algorithms tested (such as a gyroscope or a magnetometer). The algorithm using only accelerometer data also seems to be the algorithm with the lowest power consumption and should be preferred. Moreover, its power consumption can be reduced by more than the increase in the error when reducing the rate of the data output by the sensor. In this work, a reduction in the data rate from 100 Hz to 10 Hz increased the RMSE by a factor of 1.8 but could reduce the power consumption associated with the sensor and the algorithm’s computation by a factor of 10. Finally, the experimental results show that the higher the speed of the sensor’s motion, the higher the error obtained using only accelerometer data. Nevertheless, the algorithm that uses only accelerometer data remains well suited to rehabilitation exercises or mobility evaluations since the speed of the sensor’s movement is also moderate.