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Review of Consumer Wearables in Emotion, Stress, Meditation, Sleep, and Activity Detection and Analysis.
30 Apr 2020-
TL;DR: There is no versatile device suitable for all purposes in recognition and analysis of emotion, stress, meditation, sleep, and physical activity in off-the-shelf devices revised.
Abstract: Wearables equipped with pervasive sensors enable us to monitor physiological and behavioral signals. In this study, we revised 55 off-the-shelf devices in recognition and analysis of emotion, stress, meditation, sleep, and physical activity, especially in field studies. Their usability directly comes from the types of sensors they possess as well as the quality and availability of raw signals. We found there is no versatile device suitable for all purposes. Empatica E4 and Microsoft Band 2 are good at emotion, stress, and together with Oura Ring at sleep research. Apple, Samsung, Garmin, and Fossil smart watches are proper in activity examination, while Muse and DREEM EEG headbands are suitable for meditation.
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TL;DR: Although there is no standardized pre-processing pipeline for PPG signal processing, as PPG data are acquired and accumulated in various ways, the recently proposed machine learning-based method is expected to offer a promising solution.
Abstract: Beyond its use in a clinical environment, photoplethysmogram (PPG) is increasingly used for measuring the physiological state of an individual in daily life. This review aims to examine existing research on photoplethysmogram concerning its generation mechanisms, measurement principles, clinical applications, noise definition, pre-processing techniques, feature detection techniques, and post-processing techniques for photoplethysmogram processing, especially from an engineering point of view. We performed an extensive search with the PubMed, Google Scholar, Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, and Web of Science databases. Exclusion conditions did not include the year of publication, but articles not published in English were excluded. Based on 118 articles, we identified four main topics of enabling PPG: (A) PPG waveform, (B) PPG features and clinical applications including basic features based on the original PPG waveform, combined features of PPG, and derivative features of PPG, (C) PPG noise including motion artifact baseline wandering and hypoperfusion, and (D) PPG signal processing including PPG preprocessing, PPG peak detection, and signal quality index. The application field of photoplethysmogram has been extending from the clinical to the mobile environment. Although there is no standardized pre-processing pipeline for PPG signal processing, as PPG data are acquired and accumulated in various ways, the recently proposed machine learning-based method is expected to offer a promising solution.
43 citations
07 Sep 2021
TL;DR: In this paper, the authors proposed a system for supporting communication based on gesture recognition using non-invasive compact sensors worn by the user or deployed in the environment (e.g., bed).
Abstract: Citizens with speech and language disorders, such as Aphasia, often experience difficulties in expressing their needs. Assistive technologies for these disorders rely mostly on graphical interfaces activated by touch or gaze, which do not effectively cover all communication contexts throughout the day and may raise privacy concerns. In the scope of the AAL APH-ALARM project, our main aim is to extend communication support for users with speech and language difficulties (mainly aphasics) in the bedroom environment. We propose a system for supporting communication based on gesture recognition using non-invasive compact sensors worn by the user or deployed in the environment (e.g., bed). A first prototype was implemented using wrist-worn sensors and machine learning to recognize a small set of gestures. Initial results suggest that gesture recognition to enhance communication for people with speech and language impairments is viable, even when in bed.
4 citations
01 Jun 2021
TL;DR: In this article, a machine learning cognitive load detector from blood volume pulse (BVP) captured by a photoplethysmography (PPG) signal was developed to improve the wellbeing of cancer patients managed at home via a mobile Coaching System.
Abstract: The CAPABLE project aims to improve the wellbeing of cancer patients managed at home via a mobile Coaching System recommending physical and mental health interventions. Patient reported outcomes are important for evaluation of the efficacy of these interventions. Nevertheless a large number of surveys might be overwhelming to patients. To understand the cognitive demand caused by the surveys and to find the adequate time to prompt patients to complete them we carried out a feasibility study. In this study we developed a machine learning cognitive load detector from blood volume pulse (BVP) captured by a photoplethysmography (PPG) signal. PPG sensors are available on consumer-grade smartwatches, which we will use in our Coaching System. We found that personalised 1D convolutional neural networks trained on raw BVP signal performed better in binary high vs low cognitive load classification than the personalised Support Vector Machines trained with heart rate variability and BVP features. We investigated if the further improvements can be obtained by teacher-student semi-supervised model training, nevertheless the performance gains were not notable. In the future we will include additional context information that might aid cognitive load estimation and drive both survey design as well as the timing of the prompts.
4 citations
01 Jan 2022
TL;DR: In this paper, the authors discuss the various recent remote sleep monitoring methods using the IoT, wearable, nearable, smartphone apps, and the role of 5G in Remote sleep monitoring.
Abstract: Quality sleep is an inevitable part for the human life to have sound physical and mental health. Abnormal sleep patterns or sleep quality result in health issues. Sleep monitoring becomes mandatory for individuals to lead a healthy life. Adopting ubiquitous computing is on the rise in the healthcare sector, particularly in remote sleep monitoring. Recent advancements in sensor technology have increased the adoption of Internet of things (IoT), wearable, nearable and smartphones in remote sleep monitoring. The present remote monitoring systems heavily depend on existing communication techniques for transferring the sensor data to the remote computer or cloud. The existing communication systems have issues such as connectivity of devices, M2M communication, latency, and throughput. This chapter discusses the various recent remote sleep monitoring methods using the IoT, wearable, nearable, smartphone apps, and the role of 5G in remote sleep monitoring.
2 citations
30 Jan 2019
TL;DR: In this paper, a proof-of-concept solution for sleep detection by observing a set of ambulatory physiological parameters in a completely non-invasive manner has been proposed by using machine learning based algorithms.
Abstract: Internet of Things for medical devices is revolutionizing healthcare industry by providing platforms for data collection via cloud gateways and analytic. In this paper, we propose a process for developing a proof of concept solution for sleep detection by observing a set of ambulatory physiological parameters in a completely non-invasive manner. Observing and detecting the state of sleep and also its quality, in an objective way, has been a challenging problem that impacts many medical fields. With the solution presented here, we propose to collect physiological signals from wearable devices, which in our case consist of a smart wristband equipped with sensors and a protocol for communication with a mobile device. With machine learning based algorithms, that we developed, we are able to detect sleep from wakefulness in up to 93% of cases. The results from our study are promising with a potential for novel insights and effective methods to manage sleep disturbances and improve sleep quality.
2 citations
References
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02 Oct 2018
TL;DR: This work introduces WESAD, a new publicly available dataset for wearable stress and affect detection that bridges the gap between previous lab studies on stress and emotions, by containing three different affective states (neutral, stress, amusement).
Abstract: Affect recognition aims to detect a person's affective state based on observables, with the goal to e.g. improve human-computer interaction. Long-term stress is known to have severe implications on wellbeing, which call for continuous and automated stress monitoring systems. However, the affective computing community lacks commonly used standard datasets for wearable stress detection which a) provide multimodal high-quality data, and b) include multiple affective states. Therefore, we introduce WESAD, a new publicly available dataset for wearable stress and affect detection. This multimodal dataset features physiological and motion data, recorded from both a wrist- and a chest-worn device, of 15 subjects during a lab study. The following sensor modalities are included: blood volume pulse, electrocardiogram, electrodermal activity, electromyogram, respiration, body temperature, and three-axis acceleration. Moreover, the dataset bridges the gap between previous lab studies on stress and emotions, by containing three different affective states (neutral, stress, amusement). In addition, self-reports of the subjects, which were obtained using several established questionnaires, are contained in the dataset. Furthermore, a benchmark is created on the dataset, using well-known features and standard machine learning methods. Considering the three-class classification problem ( baseline vs. stress vs. amusement ), we achieved classification accuracies of up to 80%,. In the binary case ( stress vs. non-stress ), accuracies of up to 93%, were reached. Finally, we provide a detailed analysis and comparison of the two device locations ( chest vs. wrist ) as well as the different sensor modalities.
486 citations
TL;DR: This paper explores how these various motion sensors behave in different situations in the activity recognition process, and shows that they are each capable of taking the lead roles individually, depending on the type of activity being recognized, the body position, the used data features and the classification method employed.
Abstract: For physical activity recognition, smartphone sensors, such as an accelerometer and a gyroscope, are being utilized in many research studies. So far, particularly, the accelerometer has been extensively studied. In a few recent studies, a combination of a gyroscope, a magnetometer (in a supporting role) and an accelerometer (in a lead role) has been used with the aim to improve the recognition performance. How and when are various motion sensors, which are available on a smartphone, best used for better recognition performance, either individually or in combination? This is yet to be explored. In order to investigate this question, in this paper, we explore how these various motion sensors behave in different situations in the activity recognition process. For this purpose, we designed a data collection experiment where ten participants performed seven different activities carrying smart phones at different positions. Based on the analysis of this data set, we show that these sensors, except the magnetometer, are each capable of taking the lead roles individually, depending on the type of activity being recognized, the body position, the used data features and the classification method employed (personalized or generalized). We also show that their combination only improves the overall recognition performance when their individual performances are not very high, so that there is room for performance improvement. We have made our data set and our data collection application publicly available, thereby making our experiments reproducible.
426 citations
TL;DR: This review has summarized the features and evaluated the characteristics of a cross-section of technologies for health and sports performance according to what the technology is claimed to do, whether it has been validated and is reliable, and if it is suitable for general consumer use.
Abstract: The commercial market for technologies to monitor and improve personal health and sports performance is ever expanding. A wide range of smart watches, bands, garments, and patches with embedded sensors, small portable devices and mobile applications now exist to record and provide users with feedback on many different physical performance variables. These variables include cardiorespiratory function, movement patterns, sweat analysis, tissue oxygenation, sleep, emotional state, and changes in cognitive function following concussion. In this review, we have summarized the features and evaluated the characteristics of a cross-section of technologies for health and sports performance according to what the technology is claimed to do, whether it has been validated and is reliable, and if it is suitable for general consumer use. Consumers who are choosing new technology should consider whether it (1) produces desirable (or non-desirable) outcomes, (2) has been developed based on real-world need, and (3) has been tested and proven effective in applied studies in different settings. Among the technologies included in this review, more than half have not been validated through independent research. Only 5% of the technologies have been formally validated. Around 10% of technologies have been developed for and used in research. The value of such technologies for consumer use is debatable, however, because they may require extra time to set up and interpret the data they produce. Looking to the future, the rapidly expanding market of health and sports performance technology has much to offer consumers. To create a competitive advantage, companies producing health and performance technologies should consult with consumers to identify real-world need, and invest in research to prove the effectiveness of their products. To get the best value, consumers should carefully select such products, not only based on their personal needs, but also according to the strength of supporting evidence and effectiveness of the products.
307 citations
"Review of Consumer Wearables in Emo..." refers background in this paper
...Existing literature has focused on usefulness of commercially available devices in one [1] or two domains [2], or on validity of wearable sensors [3]....
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TL;DR: This survey will examine the recent works on stress detection in daily life which are using smartphones and wearable devices and investigate the works according to used physiological modality and their targeted environment such as office, campus, car and unrestricted daily life conditions.
255 citations
"Review of Consumer Wearables in Emo..." refers background in this paper
...Those signals can be supplemented with ACC, GYRO SKT, RSP [10–14], as well as with UV, GPS, and MIC data [15]....
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...Bio-signals related to stress include EEG, ECG, BVP, GSR, SKT, and RSP [16, 17]....
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...Data from SpO2, ECG, RSP, and MIC allow us to diagnose the sleep apnea [36–38]....
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...They can also provide other data derived from the monitored signals: HR (extracted either from BVP/PPI or ECG/RRI), STP - number of steps, RSP - respiration rate, EDR - RSP from ECG, CAL - calories burned, SP - sleep phases....
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TL;DR: The impact of stress to multiple bodily responses is surveyed and efficiency, robustness and consistency of biosignal data features across the current state of knowledge in stress detection are put on.
Abstract: This review investigates the effects of psychological stress on the human body measured through biosignals. When a potentially threatening stimulus is perceived, a cascade of physiological processes occurs mobilizing the body and nervous system to confront the imminent threat and ensure effective adaptation. Biosignals that can be measured reliably in relation to such stressors include physiological (EEG, ECG, EDA, EMG) and physical measures (respiratory rate, speech, skin temperature, pupil size, eye activity). A fundamental objective in this area of psychophysiological research is to establish reliable biosignal indices that reveal the underlying physiological mechanisms of the stress response. Motivated by the lack of comprehensive guidelines on the relationship between the multitude of biosignal features used in the literature and their corresponding behaviour during stress, in this paper, the impact of stress to multiple bodily responses is surveyed. Emphasis is put on the efficiency, robustness and consistency of biosignal data features across the current state of knowledge in stress detection. It is also explored multimodal biosignal analysis and modelling methods for deriving accurate stress correlates. This paper aims to provide a comprehensive review on biosignal patterns caused during stress conditions and reliable practical guidelines towards more efficient detection of stress.
243 citations
"Review of Consumer Wearables in Emo..." refers background in this paper
...Those signals can be supplemented with ACC, GYRO SKT, RSP [10–14], as well as with UV, GPS, and MIC data [15]....
[...]
...Bio-signals related to stress include EEG, ECG, BVP, GSR, SKT, and RSP [16, 17]....
[...]
...Data from SpO2, ECG, RSP, and MIC allow us to diagnose the sleep apnea [36–38]....
[...]
...They can also provide other data derived from the monitored signals: HR (extracted either from BVP/PPI or ECG/RRI), STP - number of steps, RSP - respiration rate, EDR - RSP from ECG, CAL - calories burned, SP - sleep phases....
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