This research aims to design and implement a novel task-based knee rehabilitation strategy through kinematic synthesis, assist-as-needed control strategy, and recovery tracking system. Experimental kinematic data collected through motion capture system are utilized as an input to the mechanism synthesis procedure. Parallel mechanisms with single degree-of-freedom are considered to generate the complex three-dimensional (3D) motions of the lower leg. An exact workspace synthesis approach is utilized, in which the implicit description of the workspace is made to be a function of the structural parameters of the serial chains of the parallel mechanism, making it easy to relate those parameters to the desired trajectory from the motion capture. The synthesis procedure resulted an exoskeleton which can guide the complex motion of the human knee without the need of mimicking the joint by the exoskeleton counterpart. This can potentially reduce the improper alignment problems arising due to the constantly varying axis of rotation of human joint, which is often very difficult to predict. An assist-as-needed control and recovery tracking strategy is outlined based on an electromyography (EMG) signals and force sensing resistors (FSRs) mounted on the user and exoskeleton, respectively. The EMG signal is captured from the user leg and FSRs are applied at the attachment area of the exoskeleton and the leg, this helps to get the amount of force applied by the exoskeleton to the leg as well as for the recovery tracking. The assist-as-needed controller eliminates the need of constant supervision, and hence saves time and reduces cost of the rehabilitation process. Similarly, the real-time progress tracking system will motivate and actively engage users

Neuro-muscular disorders and diseases such as cerebral palsy and Duchenne Muscular Dystrophy can severely limit a person’s ability to perform activities of daily living (ADL). Exoskeletons can provide an active or passive support solution to assist these groups of people to perform ADL. This study presents an artificial neural network-trained adaptive controller mechanism that uses surface electromyography (sEMG) signals from the human forearm to detect hand gestures and navigate an in-house-built wheelchair-mounted upper limb robotic exoskeleton based on the user’s intent while ensuring safety. To achieve the desired position of the exoskeleton based on human intent, 10 hand gestures were recorded from 8 participants without upper limb movement disabilities. Participants were tasked to perform water bottle pick and place activities while using the exoskeleton, and sEMG signals were collected from the forearm and processed through root mean square, median filter, and mean feature extractors prior to training a scaled conjugate gradient backpropagation artificial neural network. The trained network achieved an average of more than 93% accuracy, while all 8 participants who did not have any prior experience of using an exoskeleton were successfully able to perform the task in less than 20 s using the proposed artificial neural network-trained adaptive controller mechanism. These results are significant and promising thus could be tested on people with muscular dystrophy and neuro-degenerative diseases.

Neuromuscular and sensorimotor degeneration caused by stroke or any other disease significantly reduce the physical, cognitive, and social well-being across the life span. Mostly, therapeutic interventions are employed in order to restore the lost degrees-of-freedom (DOF) caused by such impairments and automating these therapeutic tasks through exoskeletons/robots is becoming a common practice. However, aligning these robotic devices with the complex anatomical and geometrical motions of the joints is very challenging. At the same time, a good alignment is required in order to establish a better synergy of human-exoskeleton system for an effective intervention procedure. In this paper, a case study of an exoskeleton and shoulder joint alignment were studied through different size and orientation impairment models through motion capture data and musculoskeletal modeling in OpenSim. A preliminary result indicates that shoulder elevation is very sensitive to misalignment and varies with shoulder joint axes orientation; this is partly due to drastic displacement of the upper arm axes with respect to the shoulder joint origin during elevation. Additional study and analysis is required to learn any possible restraint on shoulder elevation that could potentially help in the exoskeleton development.

We illustrate the case of a 71-year-old male who initially presented with sudden onset muscle weakness and ambulation difficulty. Following medication discontinuation and additional clinical studies, he failed to improve and was admitted to the hospital 11 weeks later. He had an associated 20-pound weight loss, sudorrhea, and muscle stiffness only when weight-bearing. A complete connective tissue cascade and a paraneoplastic panel were obtained. Clinical diagnosis of acquired neuromyotonia, or Isaacs syndrome (IS), was made, and he began experiencing significant improvement after intravenous steroid infusion. IS is a rare disease that has been poorly documented in the literature. There have only been a limited number of cases which are globally documented. One of the difficulties is a lack of definite autoantibody with which to correlate the disease; however, there has been some correlation linking the disease to voltage-gated potassium channels. Ultimately, the diagnosis should be driven by history and clinical presentation. The aim of this case report is to highlight a rare disease process and increase awareness among clinicians. We also describe the associated evaluation and recommended treatment for an optimal patient outcome.

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