This paper proposes an innovative approach to generate a generalized myocardial ischemia database by modeling the virtual electrophysiology of the heart and the 12-lead electrocardiography projected by the in-silico model can serve as a ready-to-use database for automatic myocardial infarction/ischemia (MI) localization and classification. Although the virtual heart can be created by an established technique combining the cell model with personalized heart geometry to observe the spatial propagation of depolarization and repolarization waves, we developed a strategy based on the clinical pathophysiology of MI to generate a heterogeneous database with a generic heart while maintaining clinical relevance and reduced computational complexity. First, the virtual heart is simplified into 11 regions that match the types and locations, which can be diagnosed by 12-lead ECG; the major arteries were divided into 3-5 segments from the upstream to the downstream based on the general anatomy. Second, the stenosis or infarction of the major or minor coronary artery branches can cause different perfusion drops and infarct sizes. We simulated the ischemic sites in different branches of the arteries by meandering the infarction location to elaborate on possible ECG representations, which alters the infraction's size and changes the transmembrane potential (TMP) of the myocytes associated with different levels of perfusion drop. A total of 8190 different case combinations of cardiac potentials with ischemia and MI were simulated, and the corresponding ECGs were generated by forward calculations. Finally, we trained and validated our in-silico database with a sparse representation classification (SRC) and tested the transferability of the model on the real-world Physikalisch Technische Bundesanstalt (PTB) database. The overall accuracies for localizing the MI region on the PTB data achieved 0.86, which is only 2% drop compared to that derived from the simulated database (0.88). In summary, we have shown a proof-of-concept for transferring an in-silico model to real-world database to compensate for insufficient data.

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.