Lower extremity amputees suffer from mobility limitations which will result in a degradation of their quality of life. Wearable sensors are frequently used to assess spatio-temporal, kinematic and kinetic parameters providing the means to establish an interactive control of the amputee-prosthesis-environment system. Gait events and the gait phase detection of an amputee's locomotion are vital for controlling lower limb prosthetic devices. The paper presents an approach to real-time gait event detection for lower limb amputees using a wireless gyroscope attached to the shank when performing level ground and ramp activities. The results were validated using both healthy and amputee subjects and showed that the time differences in identifying Initial Contact (IC) and Toe Off (TO) events were larger in a transfemoral amputee when compared to the control subjects and a transtibial amputee (TTA). Overall, the time difference latency lies within a range of ±50 ms while the detection rate was 100% for all activities. Based on the validated results, the IC and TO events can be accurately detected using the proposed system in both control subjects and amputees when performing activities of daily living and can also be utilized in the clinical setup for rehabilitation and assessing the performance of lower limb prosthesis users.

This study presents an analysis and evaluation of gait asymmetry (GA) based on the temporal gait parameters identified using a portable gait event detection system, placed on the lateral side of the shank of both lower extremities of the participants. Assessment of GA was carried out with seven control subjects (CS), one transfemoral amputee (TFA) and one transtibial amputee (TTA) while walking at different speeds on overground (OG) and treadmill (TM). Gait cycle duration (GCD), stance phase duration (SPD), swing phase duration (SwPD), and the sub-phases of the gait cycle (GC) such as Loading-Response (LR), Foot-Flat (FF), and Push-Off (PO), Swing-1 (SW-1) and Swing-2 (SW-2) were evaluated. The results revealed that GCD showed less asymmetry as compared to other temporal parameters in both groups. A significant difference (p < 0.05) was observed between the groups for SPD and SwPD with lower limb amputees (LLA) having a longer stance and shorter swing phase for their intact side compared to their amputated side, resulting, large GA for TFA compared to CS and TTA. The findings could potentially contribute towards a better understanding of gait characteristics in LLA and provide a guide in the design and control of lower limb prosthetics/orthotics.

The integration of advanced control systems in prostheses necessitates the accurate identification of human locomotion activities, a task that can significantly benefit from EEG-based measurements combined with machine learning techniques. The main contribution of this study is the development of a novel framework for the recognition and classification of locomotion activities using electroencephalography (EEG) data by comparing the performance of different machine learning algorithms. Data of the lower limb movements during level ground walking as well as going up stairs, down stairs, up ramps, and down ramps were collected from 10 healthy volunteers. Time- and frequency-domain features were extracted by applying independent component analysis (ICA). Successively, they were used to train and test random forest and k-nearest neighbors (kNN) algorithms. For the classification, random forest revealed itself as the best-performing one, achieving an overall accuracy up to 92%. The findings of this study contribute to the field of assistive robotics by confirming that EEG-based measurements, when combined with appropriate machine learning models, can serve as robust inputs for prosthesis control systems.

The control of active prostheses and orthoses requires the precise classification of instantaneous human activity and the detection of specific events within each activity. Furthermore, such classification helps physiotherapists, orthopedists, and neurologists in kinetic/kinematic analyses of patients’ gaits. To address this need, we propose an innovative deep neural network (DNN)-based approach with a two-step hyperparameter optimization scheme for classifying human activity and gait events, specific for different motor activities, by using the ENABL3S dataset. The proposed architecture sets the baseline accuracy to 93% with a single hidden layer and offers further improvement by adding more layers; however, the corresponding number of input neurons remains a crucial hyperparameter. Our two-step hyperparameter-tuning strategy is employed which first searches for an appropriate number of hidden layers and then carefully modulates the number of neurons within these layers using 10-fold cross-validation. This multi-class classifier significantly outperforms prior machine learning algorithms for both activity and gait event recognition. Notably, our proposed scheme achieves impressive accuracy rates of 98.1% and 99.96% for human activity and gait events per activity, respectively, potentially leading to significant advancements in prosthetic/orthotic controls, patient care, and rehabilitation programs’ definition.

The central aim of this research is to propose a solution to rehabilitate historic places using a robotic manipulator. In this pursuit, the objective is to clean flat or curved surfaces of mihrab/muqarnas and to write lost Arabic inscriptions on spandrel using 7 Degree of freedom (DOF) robotic manipulator. Inverse kinematics solution for 7 DOF is derived by fixing redundant joint and solving the remaining 6 DOF inverse kinematics. Image processing techniques are used to get data from the given image for the robotic calligraphy operation on the spandrel to restore the lost inscriptions. Path planning exercise is implemented to navigate the end-effector on the desired path to write lost inscriptions and to clean curved surfaces of muqarnas. Proposed solutions are verified by simulation using MATLAB. The results showed that the manipulator successfully tracked the given path to perform the desired operation in mihrab as well as calligraphy operation on spandrel.