ABSTRACT
The topic of non-contact diagnosis became a hot topic during COVID-19 and online consultation gained popularity. In this research, a deep learning-based autonomous limb evaluation system is developed for online consultation and remote rehabilitation training for people with physical limitations. Its main goal is to collect and analyze information about limb states. The patient can evaluate the limb state at home using the mobile app, and the doctor can view the data and connect with the patient via the web's chat module to offer diagnostic opinions. Deep learning is used for the Start/End Attitude Determination Model and OpenCV for the limb and hand evaluation model, with the results being uploaded to the server. © The Authors. Published under a Creative Commons Attribution CC-BY 3.0 License.
ABSTRACT
Over fourteen million people suffer from neuromuscular diseases in the UK such as strokes, spinal cord injuries, and Parkinson's disease etc. That means at least one in six people in the UK are living with one or more neurological conditions. In order for patients to return to normal life sooner, a rigorous rehabilitation process is needed. In hospitals, physiotherapists and neurological experts prescribe specific neurorehabilitation exercises. In most cases, patients need to schedule an appointment to receive treatment in a hospital or to have physiotherapists visit them at home. The number of neuromuscular patients has increased, resulting in longer hospital waiting times. In particular, during COVID-19, patients were not allowed to visit hospitals or have physiotherapists visit them due to government restrictions. Online guides for personalised and custom rehabilitation therapy for joint spasticity and stiffness are also not available. This paper reports the development of an IoT-based prototype system that monitors and records joint movements using sensory footwear (consisting of FSR and IMU sensors) and Kinect sensors. In addition, a prototype web portal is also being developed to record performance data during exercises at home and interact with clinicians remotely. A pilot study has been conducted with six healthy individuals and test results show that there is a strong correlation between Kinect data and FSR data in terms of coordination between joint movements. © 2022 IEEE.
ABSTRACT
The physical therapy generally requires direct assistance from therapists continuously, however, the time is very limited. Moreover, the social distancing policy in the COVID-19 pandemic period made the patient could not come to rehabilitation center for physical therapy. Remote physical therapy is suggested to reduce dependency of therapist for conducting the physical therapy. However, there is few information about the necessary parameters in lower limb monitoring of post-stroke patient. Therefore, in this paper, a review for designing a low-cost online homecare physical therapy monitoring system is proposed. Article finding had been done using online search engine Google Scholars to conclude the design of the online monitoring system. Several keywords had been used, such as “online stroke rehabilitation monitoring,” “stroke rehabilitation parameters,” “stroke monitoring Internet of Things,” and “lower limb stroke monitoring.” The results show that the necessary monitor parameters are lower limb kinematics and dynamics, which can be complimented by bio-signal data, such as EMG. The lower limb monitoring system can use IMU, muscle sensor, and footswitches to measure the necessary parameters. IMU measures the lower limb kinematics because it provides wide range of measurement. Muscle sensor, which compatible to microcontroller, measures the EMG. Lastly, the footswitches detect the gait phases, which classify the measured data for more in-depth analysis. The mentioned sensors are cheap and available in the online market of Indonesia, which is suitable to realize a low-cost lower limb monitoring system. The research finding also suggests quick and accurate feedback mechanism for improving the training quality, which the feedback is combination of therapist opinion and artificial intelligence prediction. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.