Different from the ordinary apparatus and plug-in devices on bikes, this research brings up a novel system called SaFePlay+, Smart Footwear Platform Plus. The system includes a pair of knee motion sensing module and smart pressure and motion sensing insole, analyzed module and interactive App. SaFePlay+ can immediately collect lower limbs information of users. Moreover, through the novel algorithm of wearable power analyzer, it can help users to understand their body condition in order to adjust the posture, raise the efficiency and avoid sports injury. Through the verification of the apparatus, it shows that our method can get good cadence and power performance.

Office workers usually have problems of back and neck pain because of their bad posture which they always keep. In order to solve the problems, we design a smart suit, Heath Posture Protector (HP2), and 4 serious games for rehabilitation. Besides, we use machine learning to train the motion model and apply the model in playing the serious games. We expect the user who wears the smart suit HP2 can easily play the serious games, and furthermore, they can stretch the muscles of upper body by the guidance in games to reduce or even prevent the problems of upper-body pains.

Traditional lower limb biomechanics detection system are not only expansive but difficult to monitor user in the whole day. It is not friendly to the large scale of patients with knee joint disease such as knee osteoarthritis. Therefore, we design a lightweight portable lower limbs motion monitoring, biomechanics measurement, and analysis system, Smart Footwear Platform (SaFePlay). We apply SaFePlay to estimate the index of knee joint loading, knee adduction moment (KAM), by the lever-arm approach. We get information about user's foot pressure and knee bending angle, then we can derive KAM with body parameters. We conduct an experiment to verify the accuracy of the proposed method by comparing with motion capture and 3DoF force plates systems as ground truth. The result shows that the curve of KAM from SaFePlay is similar to the ground truth. The proposed method not only makes KAM evaluation portable but also requires only lightweight devices.

We propose light-weight wearable devices, iKneeBraces, to prevent knee osteoarthritis (OA) using knee adduction moment (KAM) evaluation. iKneeBrace consists of two inertial measurement units (IMUs) to measure shin and thigh angles. KAM is estimated by ground force reaction (GRF), knee position and center of pressure position. Instead of heavy and bulky 3DoF force plates conventionally used, we propose to build a 2D input regression model using shin and thigh angles from iKneeBrace as input to infer GRF direction and further estimate KAM. We perform an experiment to evaluate the method. The results show that iKneeBrace can infer KAM similar to the ground truth in the first peak, the most important part to prevent knee OA. Furthermore, the proposed method can infer KAM in all parts if better IMUs used in iKneeBrace in the future. The proposed method not only makes KAM evaluation portable but also requires only light-weight devices.