Sensors and Systems for Physical Rehabilitation and Health Monitoring—A Review

Nascimento, Lucas Medeiros Souza do; Bonfati, Lucas Vacilotto; Freitas, Melissa La Banca; Mendes, José Jair Alves; Siqueira, Hugo Valadares; Stevan, Sérgio Luiz

doi:10.3390/s20154063

Cited by 88 publications

(64 citation statements)

References 127 publications

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“…There have been significant innovations to achieve wearability using a range of material properties, structure, and integration. For example, stretchable sensors, such as strain gauges and many novel materials embedded bands, were attached to the human chest to measure local strain realizing the respiration monitoring [ 3 , 4 , 5 ]. Bioimpedance devices have been used for measuring lung capacity [ 6 , 7 ] because bioimpedance has a linear relationship with respiratory volume during normal breathing.…”

Section: Introductionmentioning

confidence: 99%

Extraction and Analysis of Respiratory Motion Using a Comprehensive Wearable Health Monitoring System

George

Moon

Lee

2021

Sensors

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Respiratory activity is an important vital sign of life that can indicate health status. Diseases such as bronchitis, emphysema, pneumonia and coronavirus cause respiratory disorders that affect the respiratory systems. Typically, the diagnosis of these diseases is facilitated by pulmonary auscultation using a stethoscope. We present a new attempt to develop a lightweight, comprehensive wearable sensor system to monitor respiration using a multi-sensor approach. We employed new wearable sensor technology using a novel integration of acoustics and biopotentials to monitor various vital signs on two volunteers. In this study, a new method to monitor lung function, such as respiration rate and tidal volume, is presented using the multi-sensor approach. Using the new sensor, we obtained lung sound, electrocardiogram (ECG), and electromyogram (EMG) measurements at the external intercostal muscles (EIM) and at the diaphragm during breathing cycles with 500 mL, 625 mL, 750 mL, 875 mL, and 1000 mL tidal volume. The tidal volumes were controlled with a spirometer. The duration of each breathing cycle was 8 s and was timed using a metronome. For each of the different tidal volumes, the EMG data was plotted against time and the area under the curve (AUC) was calculated. The AUC calculated from EMG data obtained at the diaphragm and EIM represent the expansion of the diaphragm and EIM respectively. AUC obtained from EMG data collected at the diaphragm had a lower variance between samples per tidal volume compared to those monitored at the EIM. Using cubic spline interpolation, we built a model for computing tidal volume from EMG data at the diaphragm. Our findings show that the new sensor can be used to measure respiration rate and variations thereof and holds potential to estimate tidal lung volume from EMG measurements obtained from the diaphragm.

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Section: Introductionmentioning

confidence: 99%

Extraction and Analysis of Respiratory Motion Using a Comprehensive Wearable Health Monitoring System

George

Moon

Lee

2021

Sensors

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“…During a sports competition in real time, e-health monitoring is beneficial for determining the athlete’s response to the load of the exercise; assessing fatigue; and minimizing the risk of injury, cardiac problems, and other disorders [ 10 , 11 ]. Conventional measures of exercise load include power output, speed, time-motion analysis, global positioning system (GPS) parameters, and accelerometer-derived parameters [ 11 , 12 ]. Moreover, monitoring some hemodynamic responses, the cardiac rhythm, respiratory and metabolic indices, body temperature, and other settings is essential for safe exercise [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel mHealth Monitoring System during Cycling in Elite Athletes

Iliadis

Tomović

Dervas

et al. 2021

IJERPH

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Background: Cycling is a very demanding physical activity that may create various health disorders during an athlete’s career. Recently, smart mobile and wearable technologies have been used to monitor physiological responses and possible disturbances during physical activity. Thus, the application of mHealth methods in sports poses a challenge today. This study used a novel mobile-Health method to monitor athletes’ physiological responses and to detect health disorders early during cycling in elite athletes. Methods: Sixteen high-level cyclists participated in this study, which included a series of measurements in the laboratory; health and performance assessments; and then application in the field of mHealth monitoring in two training seasons, at the beginning of their training period and in the race season. A field monitoring test took place during 30 min of uphill cycling with the participant’s heart rate at the ventilatory threshold. During monitoring periods, heart rate, oxygen saturation, respiratory rate, and electrocardiogram were monitored via the mHealth system. Moreover, the SpO2 was estimated continuously, and the symptoms during effort were reported. Results: A significant correlation was found between the symptoms reported by the athletes in the two field tests and the findings recorded with the application of the mHealth monitoring method. However, from the pre-participation screening in the laboratory and from the spiroergometric tests, no abnormal findings were detected that were to blame for the appearance of the symptoms. Conclusions: The application of mHealth monitoring during competitive cycling is a very useful method for the early recording of cardiac and other health disorders of athletes, whose untimely evaluation could lead to unforeseen events.

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“…Reliably tracking changes in motor function yields useful information to guide the rehabilitation process of patients with disease related movement restrictions [ 6 , 7 , 8 ]. Do Nascimento et al, (2020) were able to show in their review paper that IMUs are among the most frequently used sensors in rehabilitation with a share of 14% [ 9 ]. The authors explain this high proportion by the increasing availability of IMU sensors, the decreasing cost of these devices, and their increasingly proven effectiveness in remote monitoring and supervision of exercises and everyday activities.…”

Section: Introductionmentioning

confidence: 99%

“…The authors explain this high proportion by the increasing availability of IMU sensors, the decreasing cost of these devices, and their increasingly proven effectiveness in remote monitoring and supervision of exercises and everyday activities. Less frequently, in only 7% of all publications in the field of rehabilitation, were surface electromyography sensors (sEMG sensors) used, whereby no distinction is made by do Nascimento et al between sEMG sensors using self-adhesive pre-gelled sEMG electrodes and dry electrodes [ 9 ]. However, particularly in the case of neurological diseases associated with motor impairments, information about the activation of muscles during the performance of tasks is crucial [ 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Introduction of a sEMG Sensor System for Autonomous Use by Inexperienced Users

Avila

Junker

Disselhorst‐Klug

2020

Sensors

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Wearable devices play an increasing role in the rehabilitation of patients with movement disorders. Although information about muscular activation is highly interesting, no approach exists that allows reliable collection of this information when the sensor is applied autonomously by the patient. This paper aims to demonstrate the proof-of-principle of an innovative sEMG sensor system, which can be used intuitively by patients while detecting their muscular activation with sufficient accuracy. The sEMG sensor system utilizes a multichannel approach based on 16 sEMG leads arranged circularly around the limb. Its design enables a stable contact between the skin surface and the system’s dry electrodes, fulfills the SENIAM recommendations regarding the electrode size and inter-electrode distance and facilitates a high temporal resolution. The proof-of-principle was demonstrated by elbow flexion/extension movements of 10 subjects, proving that it has root mean square values and a signal-to-noise ratio comparable to commercial systems based on pre-gelled electrodes. Furthermore, it can be easily placed and removed by patients with reduced arm function and without detailed knowledge about the exact positioning of the sEMG electrodes. With its features, the demonstration of the sEMG sensor system’s proof-of-principle positions it as a wearable device that has the potential to monitor muscular activation in home and community settings.

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Sensors and Systems for Physical Rehabilitation and Health Monitoring—A Review

Cited by 88 publications

References 127 publications

Extraction and Analysis of Respiratory Motion Using a Comprehensive Wearable Health Monitoring System

Extraction and Analysis of Respiratory Motion Using a Comprehensive Wearable Health Monitoring System

A Novel mHealth Monitoring System during Cycling in Elite Athletes

Introduction of a sEMG Sensor System for Autonomous Use by Inexperienced Users

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