Leo Russo

Exoskeletons are a fast-growing technology that enables multiple use-cases in clinical scenarios. They can be useful tools for the rehabilitation of patients with motor dysfunctions caused by neurological conditions, aging or trauma. Assistive exoskeletons modulate the torque exerted by the electrical motors moving their joints to allow the patients wearing them to achieve an intended movement, such as gait, correctly. Their effectiveness, therefore, requires accurate online control of such torques to complement those generated by the patient. Hereby we explored Deep Learning (DL) models to generate an online prediction of the gait phase, i.e., stance or swing, during assisted walking with a lower-limb exoskeleton based on surface electromyography (sEMG) data. We leveraged the lead of muscular activation with respect to the movement of the limbs to adjust the labeling based on joints kinematics. The cross-subject design allowed to generalize over subjects not considered for training A hyperparameter optimization algorithm was also implemented to further explore the capabilities of DL models of a reduced size. We simulated a use case scenario to assess whether online implementation of the proposed technique is feasible. We also proposed a new metric called trade-of score (TOS) for evaluating the cost-performance compromise of the optimized models which lead to identifying a DL model capable of classifying gait phases with an accuracy of about 95% while significantly reducing the number of parameters compared to the full architecture. Its mean computational time of less than 10 ms offers the opportunity for accurate, online exoskeleton control based on sEMG data.

Initially developed for entertainment, the Kinect, has become widely used in robotics, rehabilitation, and human-computer interaction thanks to its RGB-D image capture and body tracking capabilities. However, professional applications reveal limitations such as quantization noise, low resolution, and occlusion, which can compromise accuracy. A potential solution is the use of a properly calibrated Kinect network to ensure consistent output across cameras. This study proposes a flexible extrinsic calibration procedure for Kinect Azure cameras, based on 2D checkerboard corner detection. This method allows independent calibration of each camera without requiring a master-slave configuration. Calibration was tested with two- and four-camera setups, positioned at heights of 1.2 m and 2 m, with varying distances (1–2.29 m) and orientations relative to the checkerboard. Results showed high accuracy, with mean calibration errors (CE) below 1 cm under optimal conditions, reaching a minimum of 3.3 mm when the checkerboard was placed 1.75 m from the cameras. The proposed method proved effective in multi-camera setups, providing large calibration volumes. This study highlights the feasibility and flexibility of the proposed approach, with potential for future research with different sensors and varying conditions.