Accidental fall is a significant health risk among the elderly. However, most of the fall detection systems give notification only after a fall occurs. Therefore, medical attention has shifted to fall preventive measures to reduce risks of fall and prevent any damage entirely. As most fall prediction data in previous literature are obtained from inertial sensors or static pressure sensors, in this study, wireless pressure sensors embedded insoles are used to train machine learning (ML) models to predict the risk of fall of an individual. The novelty of this paper is that dynamic walking data is obtained by wearing smart pressure insoles from 1101 subjects. We applied six different ML models, i.e., support vector machine (SVM), random forest (RF), logistic regression (LR), naive bayes (NB), decision tree (DT), and k-nearest neighbor (kNN). Results show that LR model with oversampling techniques achieved the highest area under curve (AUC) of 0.82, whereas the RF model with oversampling achieved the highest accuracy of 0.81 and specificity of 0.88. The results show that such models combined with pressure embedded wireless sensor insoles are capable for fall risk prediction.

Diabetes is a significant global health issue impacting millions. Approximately 26 million diabetics experience foot ulcers, with 20% ending up with amputations, resulting in high morbidity, mortality, and costs. Plantar pressure screening shows potential for early detection of Diabetic Foot Ulcers (DFUs). Although foot ulcers often occur due to excessive pressure on the soles during dynamic activities, most studies focus on static pressure measurements. This study’s primary objective is to apply wireless plantar pressure sensor-embedded insoles to classify and detect diabetic feet from healthy ones based on dynamic plantar pressure. The secondary objective is to compare statistical-based and Machine Learning (ML) classification methods. Data from 150 subjects were collected from the insoles during walking, revealing that diabetic feet have higher plantar pressure than healthy feet, which is consistent with prior research. The Adaptive Boosting (AdaBoost) ML model achieved the highest accuracy of 0.85, outperforming the statistical method, which had an accuracy of 0.67. These findings suggest that ML models, combined with pressure sensor-embedded insoles, can effectively classify healthy and diabetic feet using plantar pressure features. Future research will focus on using these insoles with ML to classify various stages of diabetic neuropathy, aiming for early prediction of foot ulcers in home settings.

Diabetes is a significant global health issue impacting millions. Approximately 26 million diabetics experience foot ulcers, with 20% leading to amputations, resulting in high morbidity, mortality, and costs. Plantar pressure screening shows potential for the early detection of Diabetic Foot Ulcers (DFUs). Although foot ulcers often occur due to excessive pressure on the soles during dynamic activities, most studies focus on static pressure measurements. This study’s primary objective is to apply wireless plantar pressure sensor-embedded insoles to classify and detect diabetic feet from healthy ones based on dynamic plantar pressure. The secondary objective is to compare statistical-based and Machine Learning (ML) classification methods. Data from 150 subjects were collected using the insoles during walking, revealing that diabetic feet have higher plantar pressure than healthy feet, consistent with prior research. The Adaptive Boosting (AdaBoost) ML model achieved the highest accuracy of 0.85, outperforming the statistical method, which had an accuracy of 0.67. These findings suggest that ML models, combined with pressure sensor-embedded insoles, can effectively classify healthy and diabetic feet using plantar pressure features. Future research will focus on using these insoles with ML to classify various stages of diabetic neuropathy, aiming for early prediction of foot ulcers in home settings.

Background. Transferring energy from the lower extremities to the torso is a crucial mechanism enabling an effective golf swing. However, the optimal plantar pressure distribution that facilitates this energy flow has not been thoroughly investigated. Objectives. This study explored the relationship and predictive potential between peak in-shoe plantar pressures at specific ...