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.

Rehabilitation after a stroke is vital for regaining functional abilities. However, a shortage of rehabilitation professionals leads to many patients with severe disabilities. Traditional rehabilitation methods can be time-consuming and hard to measure for progress. This study introduces an innovative machine learning (ML) approach for lower limb rehabilitation in stroke patients. The proposed methodology integrates two models: a fuzzy logic rule-based system and a K-Nearest Neighbor(K-NN) machine learning model. The rule-based model utilizes the Fugl-Meyer Assessment to evaluate lower limb angles during exercises using a camera without human intervention. The hybrid fuzzy logic-based ML model continuously tracks the desired angle, counts exercise repetitions, and provides real-time feedback on patient progress. Furthermore, it measures the Range of Motion (ROM) for each repetition, presenting a graphical visualization of ROMs for ten repetitions simultaneously. The model facilitates real-time evaluation of rehabilitation progress by clinicians, with the lowest observed error rate of $$0.34^\circ$$ of angle measurement. The K-NN model assesses rehabilitation exercise accuracy levels, presenting results graphically, with machine learning accuracy rates of $$97\%$$ , $$92\%$$ , and $$91\%$$ for hip flexion, hip external rotation, and knee extension rehabilitation exercises. Model training utilized data from 30 experienced physical therapists at King Chulalongkorn Memorial Hospital, Bangkok, Thailand, garnering positive evaluations from rehabilitation doctors. The proposed ML-based models offer real-time and prerecorded video capabilities, enabling telerehabilitation applications. This research highlights the potential of ML-based methodologies in stroke rehabilitation to enhance accuracy, efficiency, and patient outcomes.

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.

Accurate measurement of range of motion (ROM) during physical rehabilitation is traditionally achieved using goniometers or multi-camera, marker-based motion capture systems. The latter, while highly accurate, require specialised laboratory infrastructure, are costly, and are unsuitable for home-based use. There is growing interest in markerless, camera-based alternatives that leverage artificial intelligence (AI) for joint angle estimation. This study presents a preliminary proof-of-concept evaluation of a hybrid AI vision system, combining the MediaPipe Pose framework (version 0.8.9.1, Full model variant) with a Mamdani-type fuzzy inference system, for joint angle estimation and exercise repetition counting using a single consumer-grade camera. Fourteen healthy adult rehabilitation staff members performed three exercises (elbow flexion, knee extension, and hip external rotation) simultaneously recorded by the proposed AI system and by a reference optical motion capture system (C-Motion Visual3D™, 14 calibrated infrared cameras, 53 retro-reflective markers). Reliability was assessed using two-way mixed-effects intraclass correlation coefficients (ICC[Formula: see text]) with absolute agreement. Individual-measure ICC values ranged from poor to good across joints and conditions (range: 0.005-0.68). The highest reliability was observed for the right hip external rotation ROM (individual ICC [Formula: see text]; average ICC [Formula: see text], 95% CI: 0.63-0.89). Several measures showed poor reliability, including the right elbow flexion ROM (individual ICC [Formula: see text]) and the minimum angle of the left hip external rotation (individual ICC [Formula: see text]). Confidence intervals were wide throughout. This proof of concept demonstrates that a single-camera AI system can capture general trends in joint angles during simple rehabilitation exercises in healthy adults under controlled conditions. However, reliability is inconsistent and frequently poor, and the system has not been evaluated in patient populations with pathological movement patterns. These findings do not support clinical deployment at this stage. Substantial further development and validation in patient cohorts are required before clinical or home-based use can be considered.