Study title

The project collected quantitative and qualitative data. The quantitative datasets include engineering diagrams for exoskeleton design drawings, sensor data processing algorithms and exoskeleton controlling algorithms. The knee exoskeleton prototype validation data from laboratory trial sessions consisted of quantitative datasets to include knee motion data (knee flexion/ extension data) from participants during different exercises and qualitative datasets including videos and images of participants trying the knee exoskeleton in the laboratory. Due to commercial sensitivity the data cannot be made available.Knee osteoarthritis is one of the leading causes of chronic pain and disability in older people. Rehabilitation exercise is an essential treatment to reduce osteoarthritis pain, improve knee function and increase mobility. It is important for clinicians to be able to monitor certain signals such as load (weight) and motion during the exercise, so that they can develop a personalised rehabilitation plan for each patient. Currently, clinicians have no access to these signals and they have to use questionnaires and simple functional tests to evaluate the effect of the exercises. This relies heavily on individual experience rather than personalised monitoring, so patients often do not receive the best treatment to meet their needs. This project will develop a knee device to support and monitor rehabilitation and provide scientific evidence for clinicians to evaluate the rehabilitation progress for their patients. This will ensure that patients get the best rehabilitation treatment which will relieve pain, improve overall physical knee function and prevent disability. Patients will wear the device during their rehabilitation exercises and daily activities. Real-time feedback from the device will enable patients to monitor and manage their rehabilitation progress. Physiotherapists can adjust the exercise programme remotely to meet the patients' individual needs...