iAware: A Real-Time Emotional Biofeedback System Based on Physiological Signals

Albraikan, Amani Abdulrahman; Hafidh, Basim; Saddik, Abdulmotaleb El

doi:10.1109/access.2018.2885279

Cited by 22 publications

(18 citation statements)

References 47 publications

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“…There are some wearable devices developed to monitor the body’s physiological information, such as an electroencephalogram, an electrocardiogram, an EMG, or that show the respiration and heart rate [20]. These devices monitor the raw signals with low sampling rates, and their algorithms are not complex enough to get certain important features of the measuring signals [21]. Their advantages are that they have a long-term monitoring of about two to seven days [20].…”

Section: Discussionmentioning

confidence: 99%

An EMG Patch for the Real-Time Monitoring of Muscle-Fatigue Conditions During Exercise

Liu

Lin

Chen

et al. 2019

Sensors

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In recent years, wearable monitoring devices have been very popular in the health care field and are being used to avoid sport injuries during exercise. They are usually worn on the wrist, the same as sport watches, or on the chest, like an electrocardiogram patch. Common functions of these wearable devices are that they use real time to display the state of health of the body, and they are all small sized. The electromyogram (EMG) signal is usually used to show muscle activity. Thus, the EMG signal could be used to determine the muscle-fatigue conditions. In this study, the goal is to develop an EMG patch which could be worn on the lower leg, the gastrocnemius muscle, to detect real-time muscle fatigue while exercising. A micro controller unit (MCU) in the EMG patch is part of an ARM Cortex-M4 processor, which is used to measure the median frequency (MF) of an EMG signal in real time. When the muscle starts showing tiredness, the median frequency will shift to a low frequency. In order to delete the noise of the isotonic EMG signal, the EMG patch has to run the empirical mode decomposition algorithm. A two-electrode circuit was designed to measure the EMG signal. The maximum power consumption of the EMG patch was about 39.5 mAh. In order to verify that the real-time MF values measured by the EMG patch were close to the off-line MF values measured by the computer system, we used the root-mean-square value to estimate the difference in the real-time MF values and the off-line MF values. There were 20 participants that rode an exercise bicycle at different speeds. Their EMG signals were recorded with an EMG patch and a physiological measurement system at the same time. Every participant rode the exercise bicycle twice. The averaged root-mean-square values were 2.86 ± 0.86 Hz and 2.56 ± 0.47 Hz for the first and second time, respectively. Moreover, we also developed an application program implemented on a smart phone to display the participants’ muscle-fatigue conditions and information while exercising. Therefore, the EMG patch designed in this study could monitor the muscle-fatigue conditions to avoid sport injuries while exercising.

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Section: Discussionmentioning

confidence: 99%

An EMG Patch for the Real-Time Monitoring of Muscle-Fatigue Conditions During Exercise

Liu

Lin

Chen

et al. 2019

Sensors

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“…Hyperhidrosis is one of the important indicators of anxiety, rage and impulsiveness getting built up in an individual [ 3 ]. Shortness of breath, intermittent sighing and wheezing is also a highly dependable indicator of emotional disturbance [ 32 ].…”

Section: Related Workmentioning

confidence: 99%

Prescriptive analytics for impulsive behaviour prevention using real-time biometrics

et al. 2021

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“…In general, categorical and multidimensional approaches are utilized. The former distinguishes several discrete emotional categories like six types of emotions proposed by Ekman and Friesen [14] used in [15]; Plutchik's 'wheel of emotions' [16] with 8 primary emotions utilized as 8×3=24 emojis [17] (only four+neutral were actually analyzed more in-depth) or limited to only 6 basic emotions [18]. Many authors decided to apply their own emotion categories, usually as a subset or small modification of the classical ones, e.g.…”

Section: A Emotional Modelsmentioning

confidence: 99%

Emotion Recognition Using Wearables: A Systematic Literature Review - Work-in-progress

Saganowski

Dutkowiak

Dziadek³

et al. 2020

2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

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iAware: A Real-Time Emotional Biofeedback System Based on Physiological Signals

Cited by 22 publications

References 47 publications

An EMG Patch for the Real-Time Monitoring of Muscle-Fatigue Conditions During Exercise

An EMG Patch for the Real-Time Monitoring of Muscle-Fatigue Conditions During Exercise

Prescriptive analytics for impulsive behaviour prevention using real-time biometrics

Emotion Recognition Using Wearables: A Systematic Literature Review - Work-in-progress

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