Remote Health Monitoring Systems Based on Bluetooth Low Energy (BLE)
45
designed a fabric stretch sensor embedded system for muscle activity monitoring. The strain sensor resistance varies sensitively with body movements. The
designed system includes an application that shows muscle activity data and
highlights the features such as muscle movement distribution. To avoid sports
injuries during exercise, authors in [20] designed an EMG patch to supervise
the muscle fatigue conditions during isotonic contraction. The developed system
deploys two electrodes to measure the sEMG signal. A microcontroller unit in
the EMG patch is used to measure in real-time the median frequency of an EMG
signal. When the muscle is tired, the median frequency will shift to a low value.
The sensed values are sent via BLE to a mobile phone running an APP that
displays the muscle fatigue levels and the user riding information.
- Brain Activity Monitoring: Sullivan and all. [21] proposed a brain activity
monitoring system using the non-invasive electroencephalogram EEG sensor that
measures the neural electrical activity of the brain from the scalp surface. The
developed EEG monitoring system assisted by deep learning mechanism provides
information about neonatal brain health to help clinicians in neonatal EEG
abnormalities diagnosing. The proposed system uses a low-cost -low-power EEG
acquisition system including BLE interface for communication. Besides that,
the authors developed an Android app visualizing single-channel EEG and the
neonatal seizure presence. A deep convolutional neural network and an algorithm
for EEG sonification used to perceive EEG morphology changes.
- Breath Rate Monitoring: Authors in [22] developed a new system to supervise in real-time the respiratory signal. The developed system includes three
parts: smart belts, a display unit, and an online storage unit. A textile-based
pressure fabric attached to a belt converting the stomach movement into an electrical signal that is transmitted via BLE to a remote station where it is displayed
in real-time and uploaded to an online repository for future analysis. The authors
tested the performance of the system when individuals performed activities like
talking and walking. In [23] authors developed a system detecting sleep disorders
such as respiratory flow repetitive cessations during sleep using a magnetometer
sensor placed onto the body detecting millimetre night-time breathing movements by measuring the change in the magnetic vectors. The developed system
includes a noninvasive wearable sensor, a wireless BLE module and a low-power
microcontroller.
Table 1 gives a summary of all mono-sensing RHMS based BLE system.
Multi-sensing RHMS. This subsection analyzes related works about RHMS
that sense and collect physiological informations from more than one physioligical sensor.
- Cardiovascular RHMS Using Multi-sensing: Authors in [30] designed an
epidermal patch, which is called Chem-Phys that offers simultaneous real-time
monitoring of a biochemical (lactate) and an electrophysiological signal (electrocardiogram) for fitness monitoring. Besides that, for monitoring the cardiovascular disease authors in [31] designed wearable devices such as ECG and heart
45
designed a fabric stretch sensor embedded system for muscle activity monitoring. The strain sensor resistance varies sensitively with body movements. The
designed system includes an application that shows muscle activity data and
highlights the features such as muscle movement distribution. To avoid sports
injuries during exercise, authors in [20] designed an EMG patch to supervise
the muscle fatigue conditions during isotonic contraction. The developed system
deploys two electrodes to measure the sEMG signal. A microcontroller unit in
the EMG patch is used to measure in real-time the median frequency of an EMG
signal. When the muscle is tired, the median frequency will shift to a low value.
The sensed values are sent via BLE to a mobile phone running an APP that
displays the muscle fatigue levels and the user riding information.
- Brain Activity Monitoring: Sullivan and all. [21] proposed a brain activity
monitoring system using the non-invasive electroencephalogram EEG sensor that
measures the neural electrical activity of the brain from the scalp surface. The
developed EEG monitoring system assisted by deep learning mechanism provides
information about neonatal brain health to help clinicians in neonatal EEG
abnormalities diagnosing. The proposed system uses a low-cost -low-power EEG
acquisition system including BLE interface for communication. Besides that,
the authors developed an Android app visualizing single-channel EEG and the
neonatal seizure presence. A deep convolutional neural network and an algorithm
for EEG sonification used to perceive EEG morphology changes.
- Breath Rate Monitoring: Authors in [22] developed a new system to supervise in real-time the respiratory signal. The developed system includes three
parts: smart belts, a display unit, and an online storage unit. A textile-based
pressure fabric attached to a belt converting the stomach movement into an electrical signal that is transmitted via BLE to a remote station where it is displayed
in real-time and uploaded to an online repository for future analysis. The authors
tested the performance of the system when individuals performed activities like
talking and walking. In [23] authors developed a system detecting sleep disorders
such as respiratory flow repetitive cessations during sleep using a magnetometer
sensor placed onto the body detecting millimetre night-time breathing movements by measuring the change in the magnetic vectors. The developed system
includes a noninvasive wearable sensor, a wireless BLE module and a low-power
microcontroller.
Table 1 gives a summary of all mono-sensing RHMS based BLE system.
Multi-sensing RHMS. This subsection analyzes related works about RHMS
that sense and collect physiological informations from more than one physioligical sensor.
- Cardiovascular RHMS Using Multi-sensing: Authors in [30] designed an
epidermal patch, which is called Chem-Phys that offers simultaneous real-time
monitoring of a biochemical (lactate) and an electrophysiological signal (electrocardiogram) for fitness monitoring. Besides that, for monitoring the cardiovascular disease authors in [31] designed wearable devices such as ECG and heart