Comparing different methods of gait speed estimation using wearable sensors in individuals with varying levels of mobility impairments

Deep Learning Methods for Speed Estimation of Bipedal Motion from Wearable IMU Sensors

“…We will compare all tested deep-learning methods with state-of-the-art approaches based on heuristic/feature-based approaches. We have used the recent survey [ 24 ] for selecting the candidates. The methods are based on the integration of the speed from the acceleration sensor, using different features, such as detected heel-strikes for each limb, to calibrate the integration.…”

“…The methods are based on the integration of the speed from the acceleration sensor, using different features, such as detected heel-strikes for each limb, to calibrate the integration. Ten different variants of the features and their data processing are compared in [ 24 ], called method 1 to method 10, with the increasing complexity of the processing. The best performing method for their data was method 10 that uses information from the wrist sensor.…”

“…Since we do not have sensors on the wrist, we compared only those not using their signals, i.e., methods in Table 5 . The parameters of all relevant methods of [ 24 ]—i.e., the complementary filter cut frequency, the gyroscope scale error compensation parameter, and the accelerometer scale error compensation parameter—were optimized using Matlab’s fminsearch method for best accuracy ( 8 ) for each method independently.…”

“…The best performance that was obtained on our data is Method 4 of [ 24 ], which uses the mid-stance to mid-stance segmentation to obtain the heel strike and toe-off indices. The estimation of yaw angle was manually set to zero constant because only the forward speed is measured.…”

“…However, since our primary aim is to learn from the raw data, we will consider feature-based methods as a baseline for comparison. From a wide range of available methods, we analyze the performance of methods mentioned in survey [ 24 ], including extensive parameter tuning. Moreover, the default sensor placement in these approaches is almost always determined to be the foot attachment [ 4 ], with few exception such as [ 25 ].…”