Relieving the burden of intensive labeling for stress monitoring in the wild by using semi-supervised learning.

Stress, a natural process affecting individuals' wellbeing, has a profound impact on overall quality of life. Researchers from diverse fields employ various technologies and methodologies to investigate it and alleviate the negative effects of this phenomenon. Wearable devices, such as smart bands, capture physiological data, including heart rate variability, motions, and electrodermal activity, enabling stress level monitoring through machine learning models. However, labeling data for model accuracy assessment poses a significant challenge in stress-related research due to incomplete or inaccurate labels provided by individuals in their daily lives. To address this labeling predicament, our study proposes implementing Semi-Supervised Learning (SSL) models. Through comparisons with deep learning-based supervised models and clustering-based unsupervised models, we evaluate the performance of our SSL models. Our experiments show that our SSL models achieve 77% accuracy with a classifier trained on an augmented dataset prepared using the label propagation (LP) algorithm. Additionally, our deep autoencoder network achieves 76% accuracy. These results highlight the superiority of SSL models over unsupervised learning techniques and their comparable performance to supervised learning models, even with limited labeled data. By relieving the burden of labeling in daily life stress recognition, our study advances stress-related research, recognizing stress as a natural process rather than a disease. This facilitates the development of more efficient and accurate stress monitoring methods in the wild.

Validation of an IMU Gait Analysis Algorithm for Gait Monitoring in Daily Life Situations.

Gait is an essential function for humans, and gait patterns in daily life provide meaningful information about a person's cognitive and physical health conditions. Inertial measurement units (IMUs) have emerged as a promising tool for low-cost, unobtrusive gait analysis. However, large varieties of IMU gait analysis algorithms and the lack of consensus for their validation make it difficult for researchers to assess the reliability of the algorithms for specific use cases. In daily life, individuals adapt their gait patterns in response to changes in the environment, making it necessary for IMU gait analysis algorithms to provide accurate measurements despite these gait variations. In this paper, we reviewed common types of IMU gait analysis algorithms and appropriate analysis methods to evaluate the accuracy of gait parameters extracted from IMU measurements. We then evaluated stride lengths and stride times calculated from a comprehensive double integration based IMU gait analysis algorithm using an optoelectric walkway as gold standard. In total, 729 strides from five healthy subjects and three different walking patterns were analyzed. Correlation analyses and Bland-Altman plots showed that this method is accurate and robust against large variations in walking patterns (stride length: correlation coefficient (r) was 0.99, root mean square error (RMSE) was 3% and average limits of agreement (LoA) was 6%; stride time: r was 0.95, RMSE was 4% and average LoA was 7%), making it suitable for gait evaluation in daily life situations. Due to the small sample size, our preliminary findings should be verified in future studies.

Biosensing and Actuation-Platforms Coupling Body Input-Output Modalities for Affective Technologies.

Research in the use of ubiquitous technologies, tracking systems and wearables within mental health domains is on the rise. In recent years, affective technologies have gained traction and garnered the interest of interdisciplinary fields as the research on such technologies matured. However, while the role of movement and bodily experience to affective experience is well-established, how to best address movement and engagement beyond measuring cues and signals in technology-driven interactions has been unclear. In a joint industry-academia effort, we aim to remodel how affective technologies can help address body and emotional self-awareness. We present an overview of biosignals that have become standard in low-cost physiological monitoring and show how these can be matched with methods and engagements used by interaction designers skilled in designing for bodily engagement and aesthetic experiences. Taking both strands of work together offers unprecedented design opportunities that inspire further research. Through first-person soma design, an approach that draws upon the designer's felt experience and puts the sentient body at the forefront, we outline a comprehensive work for the creation of novel interactions in the form of couplings that combine biosensing and body feedback modalities of relevance to affective health. These couplings lie within the creation of design toolkits that have the potential to render rich embodied interactions to the designer/user. As a result we introduce the concept of "orchestration". By orchestration, we refer to the design of the overall interaction: coupling sensors to actuation of relevance to the affective experience; initiating and closing the interaction; habituating; helping improve on the users' body awareness and engagement with emotional experiences; soothing, calming, or energising, depending on the affective health condition and the intentions of the designer. Through the creation of a range of prototypes and couplings we elicited requirements on broader orchestration mechanisms. First-person soma design lets researchers look afresh at biosignals that, when experienced through the body, are called to reshape affective technologies with novel ways to interpret biodata, feel it, understand it and reflect upon our bodies.

How We Found Our IMU: Guidelines to IMU Selection and a Comparison of Seven IMUs for Pervasive Healthcare Applications.

Inertial measurement units (IMUs) are commonly used for localization or movement tracking in pervasive healthcare-related studies, and gait analysis is one of the most often studied topics using IMUs. The increasing variety of commercially available IMU devices offers convenience by combining the sensor modalities and simplifies the data collection procedures. However, selecting the most suitable IMU device for a certain use case is increasingly challenging. In this study, guidelines for IMU selection are proposed. In particular, seven IMUs were compared in terms of their specifications, data collection procedures, and raw data quality. Data collected from the IMUs were then analyzed by a gait analysis algorithm. The difference in accuracy of the calculated gait parameters between the IMUs could be used to retrace the issues in raw data, such as acceleration range or sensor calibration. Based on our algorithm, we were able to identify the best-suited IMUs for our needs. This study provides an overview of how to select the IMUs based on the area of study with concrete examples, and gives insights into the features of seven commercial IMUs using real data.

How to Relax in Stressful Situations: A Smart Stress Reduction System.

Stress is an inescapable element of the modern age. Instances of untreated stress may lead to a reduction in the individual's health, well-being and socio-economic situation. Stress management application development for wearable smart devices is a growing market. The use of wearable smart devices and biofeedback for individualized real-life stress reduction interventions has received less attention. By using our unobtrusive automatic stress detection system for use with consumer-grade smart bands, we first detected stress levels. When a high stress level is detected, our system suggests the most appropriate relaxation method by analyzing the physical activity-based contextual information. In more restricted contexts, physical activity is lower and mobile relaxation methods might be more appropriate, whereas in free contexts traditional methods might be useful. We further compared traditional and mobile relaxation methods by using our stress level detection system during an eight day EU project training event involving 15 early stage researchers (mean age 28; gender 9 Male, 6 Female). Participants' daily stress levels were monitored and a range of traditional and mobile stress management techniques was applied. On day eight, participants were exposed to a 'stressful' event by being required to give an oral presentation. Insights about the success of both traditional and mobile relaxation methods by using the physiological signals and collected self-reports were provided.

How Laboratory Experiments Can Be Exploited forMonitoring Stress in the Wild: A Bridge BetweenLaboratory and Daily Life.

Chronic stress leads to poor well-being, and it has effects on life quality and health. Societymay have significant benefits from an automatic daily life stress detection system using unobtrusivewearable devices using physiological signals. However, the performance of these systems is notsufficiently accurate when they are used in unrestricted daily life compared to the systems testedin controlled real-life and laboratory conditions. To test our stress level detection system thatpreprocesses noisy physiological signals, extracts features, and applies machine learning classificationtechniques, we used a laboratory experiment and ecological momentary assessment based datacollection with smartwatches in daily life. We investigated the effect of different labeling techniquesand different training and test environments. In the laboratory environments, we had more controlledsituations, and we could validate the perceived stress from self-reports. When machine learningmodels were trained in the laboratory instead of training them with the data coming from daily life,the accuracy of the system when tested in daily life improved significantly. The subjectivity effectcoming from the self-reports in daily life could be eliminated. Our system obtained higher stresslevel detection accuracy results compared to most of the previous daily life studies.

Deep Learning for Fall Risk Assessment With Inertial Sensors: Utilizing Domain Knowledge in Spatio-Temporal Gait Parameters.

Fall risk assessment is essential to predict and prevent falls in geriatric populations, especially patients with life-long conditions like neurological disorders. Inertial sensor-based pervasive gait analysis systems have become viable means to facilitate continuous fall risk assessment in non-hospital settings. However, a gait analysis system is not sufficient to detect the characteristics leading to increased fall risk, and powerful inference models are required to detect the underlying factors specific to fall risk. Machine learning models and especially the recently proposed deep learning methods offer the needed predictive power. Deep neural networks have the potential to produce models that can operate directly on the raw data, thus alleviating the need for feature engineering. However, the domain knowledge inherent in the well-established spatio-temporal gait parameters are still valuable to help a model achieve high inference accuracies. In this study, we explore deep learning methods, specifically long short-term memory (LSTM) neural networks, for the problem of fall risk assessment. We utilize sequences of spatio-temporal gait parameters extracted by an inertial sensor-based gait analysis system as input features. To quantify the performance of the proposed approach, we compare it with more traditional machine learning methods. The proposed LSTM model, trained with a gait dataset collected from 60 neurological disorder patients, achieves a superior classification accuracy of 92.1% on a separate test dataset collected from 16 patients. This study serves as one of the first attempts to employ deep learning approaches in this domain and the results demonstrate their potential.

Abnormal respiratory event detection in sleep: A prescreening system with smart wearables.

Sleeping is an important activity to monitor since it has a crucial role in the overall health and well-being of the people and society. In order to diagnose the problems in sleep, different monitoring systems are developed in the literature. The unobtrusiveness, reduced cost, objectiveness, protection of privacy and user-friendliness are the main design considerations and the proposed system design achieves those objectives by utilizing smart wearables, smart watch and smart phone. The accelerometer and heart rate monitor sensors on smart watch and the sound level sensor on the smart phone are activated. The experiments with this system are performed with 17 subjects in a sleep clinic. The data collected from these subjects is used to generate various combinations by employing varied feature extraction, feature selection and sampling approaches. Five different machine learning algorithms are implemented and the classification results are generated using the various combinations of data, training and scoring strategies. The system performance is measured in two ways, the accuracy rate of distinguishing abnormal respiratory events is 85.95% and the classification success of subjects according to the problems in their respiration is one misclassification among 17 subjects. With all the methodology utilized in this study, the proposed system is a novel prescreening tool which recognizes the severity of problems in respiration during sleep.

Continuous Stress Detection Using Wearable Sensors in Real Life: Algorithmic Programming Contest Case Study.

The negative effects of mental stress on human health has been known for decades. High-level stress must be detected at early stages to prevent these negative effects. After the emergence of wearable devices that could be part of our lives, researchers have started detecting extreme stress of individuals with them during daily routines. Initial experiments were performed in laboratory environments and recently a number of works took a step outside the laboratory environment to the real-life. We developed an automatic stress detection system using physiological signals obtained from unobtrusive smart wearable devices which can be carried during the daily life routines of individuals. This system has modality-specific artifact removal and feature extraction methods for real-life conditions. We further tested our system in a real-life setting with collected physiological data from 21 participants of an algorithmic programming contest for nine days. This event had lectures, contests as well as free time. By using heart activity, skin conductance and accelerometer signals, we successfully discriminated contest stress, relatively higher cognitive load (lecture) and relaxed time activities by using different machine learning methods.

Stress detection in daily life scenarios using smart phones and wearable sensors: A survey.

Stress has become a significant cause for many diseases in the modern society. Recently, smartphones, smartwatches and smart wrist-bands have become an integral part of our lives and have reached a widespread usage. This raised the question of whether we can detect and prevent stress with smartphones and wearable sensors. In this survey, we will examine the recent works on stress detection in daily life which are using smartphones and wearable devices. Although there are a number of works related to stress detection in controlled laboratory conditions, the number of studies examining stress detection in daily life is limited. We will divide and investigate the works according to used physiological modality and their targeted environment such as office, campus, car and unrestricted daily life conditions. We will also discuss promising techniques, alleviation methods and research challenges.

Inertial Sensor-Based Robust Gait Analysis in Non-Hospital Settings for Neurological Disorders.

The gold standards for gait analysis are instrumented walkways and marker-based motion capture systems, which require costly infrastructure and are only available in hospitals and specialized gait clinics. Even though the completeness and the accuracy of these systems are unquestionable, a mobile and pervasive gait analysis alternative suitable for non-hospital settings is a clinical necessity. Using inertial sensors for gait analysis has been well explored in the literature with promising results. However, the majority of the existing work does not consider realistic conditions where data collection and sensor placement imperfections are imminent. Moreover, some of the underlying assumptions of the existing work are not compatible with pathological gait, decreasing the accuracy. To overcome these challenges, we propose a foot-mounted inertial sensor-based gait analysis system that extends the well-established zero-velocity update and Kalman filtering methodology. Our system copes with various cases of data collection difficulties and relaxes some of the assumptions invalid for pathological gait (e.g., the assumption of observing a heel strike during a gait cycle). The system is able to extract a rich set of standard gait metrics, including stride length, cadence, cycle time, stance time, swing time, stance ratio, speed, maximum/minimum clearance and turning rate. We validated the spatio-temporal accuracy of the proposed system by comparing the stride length and swing time output with an IR depth-camera-based reference system on a dataset comprised of 22 subjects. Furthermore, to highlight the clinical applicability of the system, we present a clinical discussion of the extracted metrics on a disjoint dataset of 17 subjects with various neurological conditions.

Wearable Therapy - Detecting Information from Wearables and Mobiles that are Relevant to Clinical and Self-directed Therapy.

BACKGROUND: This accompanying editorial provides a brief introduction into the focus theme "Wearable Therapy". OBJECTIVES: The focus theme "Wearable Therapy" aims to present contributions which target wearable and mobile technologies to support clinical and self-directed therapy. METHODS: A call for papers was announced to all participants of the "9th International Conference on Pervasive Computing Technologies for Healthcare" and was published in November 2015. A peer review process was conducted to select the papers for the focus theme. RESULTS: Six papers were selected to be included in this focus theme. The paper topics cover a broad range including an approach to build a health informatics research program, a comprehensive literature review of self-quantification for health self-management, methods for affective state detection of informal care givers, social-aware handling of falls, smart shoes for supporting self-directed therapy of alcohol addicts, and reference information model for pervasive health systems. CONCLUSIONS: More empirical evidence is needed that confirms sustainable effects of employing wearable and mobile technology for clinical and self-directed therapy. Inconsistencies between different conceptual approaches need to be revealed in order to enable more systematic investigations and comparisons.

Multimodal wireless sensor network-based ambient assisted living in real homes with multiple residents.

Human activity recognition and behavior monitoring in a home setting using wireless sensor networks (WSNs) provide a great potential for ambient assisted living (AAL) applications, ranging from health and wellbeing monitoring to resource consumption monitoring. However, due to the limitations of the sensor devices, challenges in wireless communication and the challenges in processing large amounts of sensor data in order to recognize complex human activities, WSN-based AAL systems are not effectively integrated in the home environment. Additionally, given the variety of sensor types and activities, selecting the most suitable set of sensors in the deployment is an important task. In order to investigate and propose solutions to such challenges, we introduce a WSN-based multimodal AAL system compatible for homes with multiple residents. Particularly, we focus on the details of the system architecture, including the challenges of sensor selection, deployment, networking and data collection and provide guidelines for the design and deployment of an effective AAL system. We also present the details of the field study we conducted, using the systems deployed in two different real home environments with multiple residents. With these systems, we are able to collect ambient sensor data from multiple homes. This data can be used to assess the wellbeing of the residents and identify deviations from everyday routines, which may be indicators of health problems. Finally, in order to elaborate on the possible applications of the proposed AAL system and to exemplify directions for processing the collected data, we provide the results of several human activity inference experiments, along with examples on how such results could be interpreted. We believe that the experiences shared in this work will contribute towards accelerating the acceptance of WSN-based AAL systems in the home setting.