A Meditation Based Cognitive Therapy (HMBCT) for Primary Insomnia: A Treatment Feasibility Pilot Study

Design and development of an intelligent wearing system for adolescent spinal orthotics

Adolescent idiopathic scoliosis is the deformity of the human spine in three-dimensional space. It is a disease with a long course and difficult to recover in a short time. Currently, spinal orthotic braces are an effective non-surgical treatment for this condition. However, existing spinal orthotic braces are still deficient. For example, existing spinal orthotics braces have no way of knowing how correct, effective, and comfortable a patient is wearing the orthotics. Based on distributed pressure monitoring technology, this paper conducts pressure acquisition and compensation research on spinal orthotics used by adolescent patients. After analyzing the principles of orthopedics and selecting monitoring points, this paper selects appropriate sensors and calibrates them. An intelligent wearable system for adolescent spinal orthotics was developed based on these findings. The experimental results show that the system can effectively monitor the process of patients wearing spinal orthotic braces. In addition, the system can compensate and visualize the pressure in real-time, so that doctors and patients can know the process and make judgments and adjustments.

A survey on the state of the art of force myography technique (FMG): analysis and assessment

Precise feedback assures precise control commands especially for assistive or rehabilitation devices. Biofeedback systems integrated with assistive or rehabilitative robotic exoskeletons tend to increase its performance and effectiveness. Therefore, there has been plenty of research in the field of biofeedback covering different aspects such as signal acquisition, conditioning, feature extraction and integration with the control system. Among several types of biofeedback systems, Force myography (FMG) technique is a promising one in terms of affordability, high classification accuracies, ease to use, and low computational cost. Compared to traditional biofeedback systems such as electromyography (EMG) which offers some invasive techniques, FMG offers a completely non-invasive solution with much less effort for preprocessing with high accuracies. This work covers the whole aspects of FMG technique in terms of signal acquisition, feature extraction, signal processing, developing the machine learning model, evaluating tools for the performance of the model. Stating the difference between real-time and offline assessment, also highlighting the main uncovered points for further study, and thus enhancing the development of this technique.