Table 1.
Characterization of the subjects that participated in the experiment.
Fig 1.
Experimental setup including a RGB-D camera (Kinect v2) mounted on a tripod, and a Qualysis system with twelve infrared cameras.
The calibrated volume for Qualysis is illustrated by the salmon-coloured blocks. The walking path carried out by the subjects, for each task included in the protocol (T1, T2 and T3), is represented by the dashed arrowed lines. The relevant distances are also indicated. The figure was adapted from the Qualysis setup image provided by LABIOMEP (Porto Biomechanics Laboratory).
Fig 2.
Retro-reflective markers placed at the subject's body.
This figure was provided by LABIOMEP (Porto Biomechanics Laboratory).
Table 2.
Description of the tasks performed during the experiment, and associated number of trials.
Fig 3.
Body joints tracked by the Kinect v2.
Table 3.
Number of frames and duration of the selected Kinect data, for each and all activities, considering all subjects, as well as each subject (mean and standard deviation).
Table 4.
Number of actual heel strikes and gait cycles performed by all subjects, per subject, and per trial, when considering all analyzed trials of task T1.
Table 5.
Kinematic measures computed over the 3-D body joint data for activity recognition, and corresponding equations.
Fig 4.
Solution for gait cycle detection, including activity recognition and heel strike estimation.
MAF stands for moving average filter.
Table 6.
Kinematic measures computed over the Qualysis 3-D data, and corresponding equations.
Fig 5.
Measures computed over Qualysis data acquired from a given subject while walking towards the Kinect.
(a) Filtered left and right foot vertical velocity, versus the elapsed time. (b) Distance between ankles, and between knees, versus the elapsed time. (c) Filtered left and right ankle velocity, versus the elapsed time. The actual left and right heel strikes are indicated in each plot.
Table 7.
Performance results achieved by the models built with different machine learning algorithms.
Table 8.
Accuracy and F1 score achieved by the final MLP model when predicting the activity over a dataset of five “never seen” subjects.
Table 9.
Window sizes used for gait cycle detection, as well as the corresponding achieved precision and sensitivity, for WF and WB trials.
Table 10.
Mean and standard deviation of the true and absolute errors for estimating heel strike instants and gait parameters, when considering all subjects, trials, and detected heel strikes/gait cycles, for WF and WB trials.
Fig 6.
Measures computed from unfiltered and filtered Kinect data, for the same subject and WF trial of Fig 5.
(a) Distance between ankles versus the elapsed time, including the indication of the estimated and actual heel strike instants. (b) Velocity of left and right ankles versus the elapsed time, including the indication of the detected left and right heel strikes.