Monitoring physical activity in medical and clinical rehabilitation, in sports environments or as a wellness indicator is helpful to measure, analyze and evaluate physiological parameters involving the correct subject’s movements. Thanks to integrated circuit (IC) technologies, wearable sensors and portable devices have expanded rapidly in monitoring physical activities in sports and tele-rehabilitation. Therefore, sensors and signal acquisition devices became essential in the tele-rehabilitation path to obtain accurate and reliable information by analyzing the acquired physiological signals. In this context, this paper provides a state-of-the-art review of the recent advances in electroencephalogram (EEG), electrocardiogram (ECG) and electromyogram (EMG) signal monitoring systems and sensors that are relevant to the field of tele-rehabilitation and health monitoring. Mostly, we focused our contribution in EMG signals to highlight its importance in rehabilitation context applications. This review focuses on analyzing the implementation of sensors and biomedical applications both in literature than in commerce. Moreover, a final review discussion about the analyzed solutions is also reported at the end of this paper to highlight the advantages of physiological monitoring systems in rehabilitation and individuate future advancements in this direction. The main contributions of this paper are (i) the presentation of interesting works in the biomedical area, mainly focusing on sensors and systems for physical rehabilitation and health monitoring between 2016 and up-to-date, and (ii) the indication of the main types of commercial sensors currently being used for biomedical applications.

OBJECTIVE Impaired insulin-stimulated myocardial glucose uptake has occurred in patients with type 2 diabetes with or without coronary artery disease. Whether cardiac insulin resistance is present remains uncertain in subjects at risk for type 2 diabetes, such as individuals with impaired glucose tolerance (IGT) or those with normal glucose tolerance (NGT) and 1-h postload glucose ≥155 mg/dL during an oral glucose tolerance test (NGT 1-h high). This issue was examined in this study. RESEARCH DESIGN AND METHODS The myocardial metabolic rate of glucose (MRGlu) was measured by using dynamic ¹⁸F-fluorodeoxyglucose positron emission tomography combined with a euglycemic-hyperinsulinemic clamp in 30 volunteers without coronary artery disease. Three groups were studied: 1) those with 1-h postload glucose <155 mg/dL (NGT 1-h low) (n = 10), 2) those with NGT 1-h high (n = 10), 3) and those with IGT (n = 10). RESULTS After adjusting for age, sex, and BMI, both subjects with NGT 1-h high (23.7 ± 6.4 mmol/min/100 mg; P = 0.024) and those with IGT (16.4 ± 6.0 mmol/min/100 mg; P < 0.0001) exhibited a significant reduction in global myocardial MRGlu; this value was 32.8 ± 9.7 mmol/min/100 mg in subjects with NGT 1-h low. Univariate correlations showed that MRGlu was positively correlated with insulin-stimulated whole-body glucose disposal (r = 0.441; P = 0.019) and negatively correlated with 1-h (r = −0.422; P = 0.025) and 2-h (r = −0.374; P = 0.05) postload glucose levels, but not with fasting glucose. CONCLUSIONS This study shows that myocardial insulin resistance is an early defect that is already detectable in individuals with dysglycemic conditions associated with an increased risk of type 2 diabetes, such as IGT and NGT 1-h high.

Parkinson's Disease (PD) is a neurodegenerative disorder that is frequently correlated with vowel articulation difficulties. The phonation problem arises in patients affected by PD is commonly known as Parkinsonian Dysarthria and identifiedby vocal signal analysis. The analysis supporte physicians and specialists in early detection and monitoring of dysarthria aiming, to increase patients life quality and to evaluate the efficacy of treatments. We investigate on vocal signal analysis correlation with speech patterns related to PD. Vowel parameters are considered as discriminant elements among PD patients and healthy subjects. Aim of this work is to define possible indicators for dysarthria in PD patients.