MUSCLE FATIGUE ANALYSIS USING EMG OBTAINED FROM IoT BASED NEURO-REHABILITATION DEVICE

Background:

A popular therapeutic strategy in neurorehabilitation includes the use of transcutaneous electrical nerve stimulation (TENS), which is a noninvasive modality that delivers small electrical impulses through electrodes into muscle tissue. This causes the muscles to contract, and when used as an adjuvant in functional tasks, can assist with the recovery time and outcome in neurorehabilitation. Several studies have shown that TENS is effective for reducing pain and improving physical function. However, the exact effect TENS has on fatigue during functional tasks is unknown since it is often evaluated in a qualitative manner. In order to fully identify the effects of fatigue in neurorehabilitation and to develop optimized control strategies, it is of particular interest to be able to quantify the effects of TENS on functional neuromuscular tasks.

Aim:

The purpose of the present study is to identify the effect of Transcutaneous Electrical Nerve Stimulation (TENS) quantitatively over both short and long term on muscle fatigue induced by a regular exercise routine from EMG obtained from a IoT based neurorehabilitation device.

Methods:

Muscle fatigue from EMG was measured as it negatively affects the recovery time experienced through rehabilitation therapy, and is a major underlying phenomenon affecting patient quality of care. In this proof of concept study, a functional resistance-based bicep curl exercises were performed with and without assisted TENS, to measure the effects on muscular performance (i.e., fatigue and fatigue resistance) using the sEMG obtained by tele rehabilitation device. The tele rehabilitation device consists of real time monitoring of cloud based system to monitor EMG in real time and IoT based mobile application to control the application of TENS.

Results:

The experiments showed that during both short-term comparison (set-to-set measurements on the same day) and long term (regularly performed over 3 months), the TENS assisted exercise shows an increased muscle fatigue resistance vs the exercise without TENS. In long term a 1.17 dB decrease in sEMG amplitude due to improved muscle resistance when compared to muscles that did not undergo stimulation.

A T-test between the power values of the two groups of data over long term (p<0.05) also showed that they were statistically significant. These results suggest that the TENS reduces muscle fatigue in the same manner as increased loading in a functional exercise. Thus, showing that TENS can be used to increase the difficulty in an exercise strategy (athletes, and post-stroke rehabilitation), and it can be optimized to create personalized therapeutic strategies based on the patient’s neuromuscular ability.

Conclusion:

An improved muscle fatigue resistance was seen in muscle exercised with TENS over the muscle exercised without TENS, thus showing that TENS can be effectively used through an IoT device to increase the difficulty in an exercise regimen for athletes and post stroke rehabilitation.