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 by analysing signals collected from the device. Patients will also get real-time muscle support from the device to help them achieve exercise goals or do daily activities such as walking, gardening or climbing stairs. With this device, older people can enjoy physical activities, living longer and more fulfilling lives.

The quantitative datasets for the knee prototype design were produced by researchers in conducting mechanical drawings, coding and data processing. The quantitative data for validating the knee prototype device (knee exoskeleton and sensing system) were collected from participants wearing the prototype and performing exercises in the laboratory. The raw data such as sensor reading and motion capture data were collected processed and validated to generate meaningful values (knee joint angles) which were used to evaluate the accuracy of the measurements from the knee prototypes. Qualitative data collection from prototype validation: Videos and images of participants trying the prototype were taken from the laboratory trial sessions. Questionnaire feedback from participants was collected after each trial session. Qualitative data collectd from other research and commercialisation activities: The Non-Disclosure Agreement from industry engagement activities was generated by the commercialisation group at the University of Leeds. The patent search reports for technology assessment were generated by the commercialisation group at the University of Leeds in conducting a desktop search. The market analysis report was produced by an external consultant in performing a desktop search.