Telerehabilitation systems that support physical therapy sessions anywhere can help save healthcare costs while also improving the quality of life of the users that need rehabilitation. The main contribution of this paper is to present, as a whole, all the features supported by the innovative Kinect-based Telerehabilitation System (KiReS). In addition to the functionalities provided by current systems, it handles two new ones that could be incorporated into them, in order to give a step forward towards a new generation of telerehabilitation systems. The knowledge extraction functionality handles knowledge about the physical therapy record of patients and treatment protocols described in an ontology, named TRHONT, to select the adequate exercises for the rehabilitation of patients. The teleimmersion functionality provides a convenient, effective and user-friendly experience when performing the telerehabilitation, through a two-way real-time multimedia communication. The ontology contains about 2300 classes and 100 properties, and the system allows a reliable transmission of Kinect video depth, audio and skeleton data, being able to adapt to various network conditions. Moreover, the system has been tested with patients who suffered from shoulder disorders or total hip replacement.

Spinal cord injury (SCI) is characterized by damage and resulting dysfunction to the motor, sensory, and/or autonomic nervous systems associated with trauma or disease processes leading to traumatic or non-traumatic SCI, respectively. The functional consequences can therefore be wide-ranging across these systems, with varying degrees of muscle paralysis, sensory impairment, and autonomic dysfunction such as problems with cardiovascular control, thermoregulation, or bowel, bladder, or sexual function [1], [2]. In general, the more rostral (higher) the damage to the spinal cord, the more body systems that will be affected. With respect to motor function, persons with damage to the cervical (neck) area of the spinal cord will have impairments to both lower and upper limb muscles and are diagnosed as having tetraplegia, while persons with damage to the thoracic (back) or lumbar (lower back) area of the spinal cord will have impairments to the muscles of the thorax and/or the lower limbs only and are diagnosed as having paraplegia. Given the functional consequences of SCI are dependent on both the severity and level of the damage to the nervous system, in addition to a variety of other factors such as pre-morbid condition, additional secondary complications, and psychosocial influences, there is a significant degree of heterogeneity in the presentation of persons with SCI [1], [2].

Paris-based Wandercraft has announced that it's latest "Atalante" exoskeleton has been updated to give paraplegic and other patients a more natural gait during rehabilitation exercises. It also received a Medical Device Regulation (MDR) certificate in Europe, allowing patients and medical staff to use the device more widely. Finally, it's taken a step closer to personal exoskeletons with additional funding. The last time I saw Wandercraft's first-generation exoskeleton was over four years ago, which is ages in the field of robotics. However, I recently got a chance to see the latest model in use with paraplegic patients, and chat with them and the team behind Atalante.

Machine learning is a form of narrow AI used to classify data and make predictions. Supervised machine learning classifies orthopaedic images comparably to humans. Neural networks identify successful exercise performance with 99.4% accuracy. Machine learning can predict successful performance of a single leg squat exercise. Unsupervised learning finds patterns in data without training; used in data mining.

This paper presents a case-based reasoning (CBR) application for discovering similar patients with non-specific musculoskeletal disorders (MSDs) and recommending treatment plans using previous experiences. From a medical perspective, MSD is a complex disorder as its cause is often bounded to a combination of physiological and psychological factors. Likewise, the features describing the condition and outcome measures vary throughout studies. However, healthcare professionals in the field work in an experience-based way, therefore we chose CBR as the core methodology for developing a decision support system for physiotherapists which would assist them in the process of their co-decision making and treatment planning. In this paper, we focus on case representation and similarity modeling for the non-specific MSD patient data as well as we conducted initial experiments on comparing patient profiles.

I was born with the usual set of limbs. When I was nine months old, I contracted meningococcal septicaemia, a dangerous infection of the blood, which very nearly killed me. I survived, but because I had sustained major tissue damage, it became necessary to amputate my right leg below the knee, all of the fingers on my left hand and the second and third digits on my right hand. I learned to walk on a prosthetic leg at the age of 14 months, and have gone through my life wearing a succession of artificial limbs. As time has passed and technology has advanced, so too have my limbs. Like our mobile phones, prostheses have become lighter, faster and more efficient. When I was nine, I was fitted with a lifeless silicone hand, a useless thing that was purely cosmetic, and so clumsy that I refused to wear it after the first day. Now, at 21, and a student in my third year at Edinburgh University, I wear a bionic arm with nimble fingers that move independently, which I operate using controlled muscle movements in my forearm, as well as an app on my phone. As a child I wore a stiff artificial leg attached with straps that frequently fell off; earlier this summer, I took delivery of a new dynamic right leg with shock absorption and carbon fibre blades. Prosthetics have been around for more than 3,000 years: wooden toes, which strapped on and were specifically designed to work with sandals, were found on the feet of Ancient Egyptian mummies.

Artificial intelligence is helping people regain their mobility after certain neurological injuries. A robotic harness controlled by a neural network offers tailored treatment that has immediately improved their ability to walk normally. To avoid persistent difficulties walking after a stroke or spinal injury, walking assistance is crucial. But this is a slow process that, if done wrong, can lead to a permanently impaired gait. In the past, several physiotherapists were needed to physically support and guide each person through the process of learning to walk again.

Scientists pioneering research at the Massachusetts Institute of Technology in Boston believe stroke victims could see movement improve by up to 15%. The team hopes to develop the robot therapy so it can help the patients regain movement in their hands and wrists, allowing them to perform tasks, such as opening jars, which are often impossible for many stroke victims. At the moment, all patients will do exercises under the instruction of a human physiotherapist to help them regain the use of limbs paralysed by a stroke. However, the response to traditional treatment can sometimes be quite poor. In the US treatment, a patient puts their hand on a robotic joystick, and is repeatedly guided through a series of movements by prompts from a "game" on a video screen.