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In this study, we proposed an intelligent health monitoring system based on smart clothing. The system consisted of smart clothing and sensing component, care institution control platform, and mobile device. The smart clothing is a wearable device for electrocardiography signal collection and heart rate monitoring. The system integrated our propose...
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... the denoised ECG signal y(t), a novel QRS complex waveform ( Figure 5) morphology analysis algorithm called MWqrs proposed in our previous work 6 was employed to differentiate between QRS complexes and artifacts. Three feature points were cal- culated after the algorithm detected a possible QRS complex. ...
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Citations
... More advanced sensors, with improved data capture and processing capabilities, are being developed to offer a deeper and more comprehensive understanding of human movement. These sensors can be embedded in a variety of wearable devices, from smart clothing [36] to bioelectronic implants [37], and are designed to provide more accurate and detailed measurements of biomechanical parameters such as movement, strength, and physiological activity. ...
The convergence among biomechanics, motor development, and wearable technology redefines our understanding of human movement. These technologies allow for the continuous monitoring of motor development and the state of motor abilities from infancy to old age, enabling early and personalized interventions to promote healthy motor skills. For athletes, they offer valuable insights to optimize technique and prevent injuries, while in old age, they help maintain mobility and prevent falls. Integration with artificial intelligence further extends these capabilities, enabling sophisticated data analysis. Wearable technology is transforming the way we approach motor development and maintenance of motor skills, offering unprecedented possibilities for improving health, performance, and quality of life at every stage of life. The promising future of these technologies paves the way for an era of more personalized and effective healthcare, driven by innovation and interdisciplinary collaboration.
... We incorporated smart clothing technology developed originally by Wang et al. [16] for monitoring electrocardiography (ECG) signals. The smart clothing was also demonstrated to be accurate for variables for older adults living in long-term care institutions in Taiwan [17] and has been used to alert persons to arrythmias that occur during exercise [18]. ...
Background
Wearable devices have the advantage of always being with individuals, enabling easy detection of their movements. Smart clothing can provide feedback to family caregivers of older adults with disabilities who require in-home care.
Methods
This study describes the process of setting up a smart technology-assisted (STA) home-nursing care program, the difficulties encountered, and strategies applied to improve the program. The STA program utilized a smart-vest, designed specifically for older persons with dementia or recovering from hip-fracture surgery. The smart-vest facilitated nurses’ and family caregivers’ detection of a care receiver’s movements via a remote-monitoring system. Movements included getting up at night, time spent in the bathroom, duration of daytime immobility, leaving the house, and daily activity. Twelve caregivers of older adults and their care receiver participated; care receivers included persons recovering from hip fracture (n = 5) and persons living with dementia (n = 7). Data about installation of the individual STA in-home systems, monitoring, and technical difficulties encountered were obtained from researchers’ reports. Qualitative data about the caregivers’ and care receivers’ use of the system were obtained from homecare nurses’ reports, which were explored with thematic analysis.
Results
Compiled reports from the research team identified three areas of difficulty with the system: incompatibility with the home environment, which caused extra hours of manpower and added to the cost of set-up and maintenance; interruptions in data transmissions, due to system malfunctions; and inaccuracies in data transmissions, due to sensors on the smart-vest. These difficulties contributed to frustration experienced by caregivers and care receivers.
Conclusions
The difficulties encountered impeded implementation of the STA home nursing care. Each of these difficulties had their own unique problems and strategies to resolve them. Our findings can provide a reference for future implementation of similar smart-home systems, which could facilitate ease-of-use for family caregivers.
... Currently, there are relatively few studies dedicated to remote health risk prediction models for the elderly using wearable sensors and DL technology. Most studies focus on developing advanced soft-or hard-ware of wearable sensor devices or improving high recognition accuracy of different daily activities for the elderly (ADL) [4][5][6][7][8][9][10][11]. These studies usually monitor the elderly's daily life through various of sensors, such as inertial measurement unit (IMU), physiological parameter sensor and ambient sensor, in the purpose of patients' essential function investigation and emergency detection. ...
With the continuous progress of the aging process, the prevalence of chronic diseases and disability among the elderly increases, resulting the corresponding medical demand and medical costs create an intensified pressure on the healthcare infrastructures. To this end, this topic proposes a remote health risk prediction model for the community- and home-based scenarios based on wearable sensor technology and deep learning technology to alleviate the pressure on healthcare infrastructure. The model is designed in three functional considerations. The wearable sensor component is responsible for remotely and continuously collecting vital signs of the elderly, including five variables: systolic blood pressure (SBP), heart rate (HR), respiratory rate (RR), temperature (TEMP), and oxygen saturation (SPO2). The neural network component consists of five different 5- input-1-output Long Short-Term Memory (LSTM) networks, which are responsible for predicting values of the vital signs. The risk prediction component consists of a simplified version of the National Early Warning Score (NEWS), which is responsible for predicting the health risk level based on the predicted values of vital signs. The model is developed and tested using existing electronic health record (EHR) that mimic vital signs data collected via wireless sensor network. We found that the model performed the best using a dataset in a size of 1000 admissions, within a time window of 12 hours, and with a configuration of 5 neurons and 50 epochs. The accuracy was over 74% for the risk level calculated from the predicted values of vital signs. Our results suggest that remote monitoring and prediction of health risks in the elderly using deep learning models is a feasible new strategy for community- and home-based monitoring systems.
... In recent years, based on these parameters, there is also a focus on developing devices, algorithms, and systems for monitoring the body's stress state (physical and psychological stress) [11][12][13]. In particular, many efforts have been made to monitor the biometric and physical information of workers in the work environment [14][15][16][17][18][19], to analogize workload and physical and psychological stress, and to utilize this information for labour management, work environment safety risk alerts and accident prevention [20][21][22][23][24][25][26][27][28][29]. ...
In recent years, Wearable Devices have been used in a wide variety of applications and fields, but because they span so many different disciplines, it is difficult to ascertain the intellectual structure of this entire research domain. No review encompasses the whole research domain related to Wearable Devices. In this study, we collected articles on wearable devices from 2001 to 2022 and quantitatively organized them by bibliometric analysis to clarify the intellectual structure of this research domain as a whole. The cluster analysis, co-occurrence analysis, and network centrality analysis were conducted on articles collected from the Web of Science. As a result, we identified one cluster that represents applied research and two clusters that represent basic research in this research domain. Furthermore, focusing on the top two countries contributing to this research domain, China and the USA., it was confirmed that China is extremely inclined toward basic research and the USA. toward applied research, indicating that applied and basic research are in balance. The basic intellectual structure of this cross-sectional research domain was identified. The results summarize the current state of research related to Wearable Devices and provide insight into trends.
... The work in [13] uses the sensors provided by an OPAL sensor to collect the acceleration generated by the body movement and, from that information and using a machine learning classifier, detect a fall. The work in [14] presents a solution based on smart clothing that collects gravitational acceleration to detect a fall using a hidden Markov model [15]. This work differs among four different states: balanced, imbalanced, falling, and normal state. ...
... Smart Bands [7,8] Clothing [9][10][11]14] Smart Phone [18,19,21] Ambient Sensors Doppler [23,24] UWB [25] Infrared [26] WiFi [27,28] Vision Depth Camera [29][30][31][32] RGB Camera [33][34][35][36][37] The second category encompasses different solutions in which the sensor is not carried by the person being monitored but, on the contrary, it is part of the environment. The work in [25] proposes the use of a non-wearable ultra-wide-band (UWB) sensor, installed in the ceiling to monitor activities underneath its area of action. ...
... The works in [30,31,48,49] focus on the intrinsic factors that cause imbalances, such as muscle strength or the ability to posture control. Age is also a very common factor pointed out by many works of the state of the art, such as [14,16,23,24,33,36,41,45,[50][51][52] or frailty [10], which is also related to age. Regarding the extrinsic factors, the presence of obstacles [44,53], bedtime [39,54], stair architecture design, and stair obstacles, such as the absence of a handrail, irregular riser height and an object left on stairs [49], are more commonly mentioned. ...
Life expectancy has increased, so the number of people in need of intensive care and attention is also growing. Falls are a major problem for older adult health, mainly because of the consequences they entail. Falls are indeed the second leading cause of unintentional death in the world. The impact on privacy, the cost, low performance, or the need to wear uncomfortable devices are the main causes for the lack of widespread solutions for fall detection and prevention. This work present a solution focused on bedtime that addresses all these causes. Bed exit is one of the most critical moments, especially when the person suffers from a cognitive impairment or has mobility problems. For this reason, this work proposes a system that monitors the position in bed in order to identify risk situations as soon as possible. This system is also combined with an automatic fall detection system. Both systems work together, in real time, offering a comprehensive solution to automatic fall detection and prevention, which is low cost and guarantees user privacy. The proposed system was experimentally validated with young adults. Results show that falls can be detected, in real time, with an accuracy of 93.51%, sensitivity of 92.04% and specificity of 95.45%. Furthermore, risk situations, such as transiting from lying on the bed to sitting on the bed side, are recognized with a 96.60% accuracy, and those where the user exits the bed are recognized with a 100% accuracy.
... The smart clothes used in this study were designed and developed by C. C. Lin. They have been implemented in nursing homes and are available commercially [16]. However, the application of smart clothes in a home setting with family caregivers who provide care to persons with dementia, or a physical disability has not been examined. ...
Background
The purpose of this preliminary study was to explore whether a smart clothes-assisted home-nursing care program could benefit family caregivers and their care recipients.
Methods
Family caregivers in charge of a care recipient’s living situation participated in this convergent parallel, mixed methods study. We recruited older persons with dementia ( n = 7) and those discharged following hip-fracture surgery ( n = 6) from neurological clinics and surgical wards of a medical center, respectively, along with their family caregivers: three spouses, eight sons, one daughter, and one daughter-in-law. Care recipients were asked to wear a smart vest at least 4 days/week for 6 months, which contained a coin-size monitor hidden in an inner pocket. Sensors installed in bedrooms and living areas received signals from the smart clothing, which were transmitted to a mobile phone app of homecare nurses, who provided caregivers with transmitted information regarding activities, emergency situations and suggestions for caregiving activities. Outcomes included changes from baseline in caregivers’ preparedness and depressive symptoms collected at 1- and 3-months, which were analyzed with Friedman’s non-parametric test of repeated measures with post-hoc analysis. Transcripts of face-to-face semi-structured interview data about caregivers’ experiences were analyzed to identify descriptive, interpretative, and pattern codes.
Results
Preparedness did not change from baseline at either 1- or 3-months for family caregivers of persons with dementia. However, depressive symptoms decreased significantly at 1-month and 3-months compared with baseline, but not between 1-months and 3-months. Analysis of the interview data revealed the smart clothes program increased family caregivers’ knowledge of the care recipient’s situation and condition, informed healthcare providers of the care recipient’s physical health and cognitive status, helped homecare nurses provide timely interventions, balanced the care recipient’s exercise and safety, motivated recipients to exercise, helped family caregivers balance work and caregiving, and provided guidance for caregiving activities.
Conclusions
Experiences with the smart clothes-assisted home-nursing care program directly benefited family caregivers, which provided indirect benefits to the care recipients due to the timely interventions and caregiving guidance from homecare nurses. These benefits suggest a smart-clothes-assisted program might be beneficial for all family caregivers.
... This extended functionality can be obtained, among other ways, by placing electronic circuits in the textiles. These textiles can perform various functions, e.g., monitoring human physiological parameters [1][2][3][4][5][6][7][8][9], ensuring communication, exchanging information [10,11], etc. For proper operation of such systems, each of its modules should be electrically connected to the other. ...
... The fastness of the electro-conductive properties of the tested strips and lines to rubbing was estimated using a motorised AATCC crock meter CBT507, a product of CBT (Poland), presented in Figure 6. The electro-conductive strip or TSL (1) ( Figure 6) is placed on a movable table (3). The table movement is ensured by a computer-controlled ...
... The stand for cyclical bending is presented in Figure 4. The stand consists of a holder for the TSL (1) equipped with a fixed (2) and a rotating clamp (3). The rotating clamp (3) is directly connected to the shaft of the stepper motor (4). ...
This article presents the results of tests on the resistance of new textile signal lines to bending and abrasion. The textile signal lines are one of the most important parts of the electronic system incorporated into modern smart garments. The main application of the lines presented in this article is the transmission of digital signals or high-frequency analogue signals. The tested lines were made of fabrics with sewn paths made of electro-conductive fabric. The construction of a measuring stand for testing the electric properties of textile transmission lines is shown. This article presents the effects of bending and abrasion on the resistance of electro-conductive strips, which are one of the elements of textile signal lines. The article also presents the effects of bending and abrasion on the characteristic impedance of constructed textile signal lines. Statistical analysis of the obtained results is also presented.
... The first selected device is a smart vest developed by Lin et al. (2018). The smart shirt is connected to the cloud and to a mobile device of a doctor. ...
... Four textrodes are embedded into the garment for ECG monitoring. The device acts as a device for surveillance, like when the ECG signal is above 140 Hz or lower than 50 Hz, an alert signal is triggered to the medical unit; or it can be used to alert when the subject is falling or emerging using a snaps when the elderly needs it [49]. ...
... In this context, the authors propose an evaluation matrix that integrates the needs elaborated previously by Imbesi et al. [12,56] with the acceptance model proposed by Tsai-Hsuan Tsai [49] to evaluate the smart clothing acceptance. ...
Smart clothing plays a big role to foster innovation and to. boost health and well-being, improving the quality of the life of people, especially when addressed to niche users with particular needs related to their health. Designing smart apparel, in order to monitor physical and physiological functions in older users, is a crucial asset that user centered design is exploring, balancing needs expressed by the users with technological requirements related to the design process. In this paper, the authors describe a user centered methodology for the design of smart garments based on the evaluation of users’ acceptance of smart clothing. This comparison method can be considered as similar to a simplified version of the quality function deployment tool, and is used to evaluate the general response of each garment typology to different categories of requirements, determining the propensity of the older user to the utilization of the developed product. The suggested methodology aims at introducing in the design process a tool to evaluate and compare developed solutions, reducing complexity in design processes by providing a tool for the comparison of significant solutions, correlating quantitative and qualitative factors.
... An ECG signal consists of several periodic segments where an R-peak is characterized with the highest upward deflection and represents a single heartbeat. The time period between two successive R-peaks represents the R-R interval [6]. The difference between two successive R-R intervals is a key component of Heart rate variability (HRV). ...
Stress is seen as an individual’s reaction to external
circumstances that are perceived as a threat. Reactions to
stress are highly subjective in nature, depending upon numerous
individualistic factors. The study of stress recovery and associated
coping efforts can help mitigate adverse health effects. Therefore,
understanding the interplay of psychological and physiological
manifestations of stress in modeling the stress recovery patterns is
of high importance. Previous studies have indicated an association
between personality traits and physiological responses. However,
definitive evidence for this association is lacking. This work
attempts to investigate the correlation between personality traits,
such as neuroticism and extraversion, and physiological responses
such as electrocardiogram and salivary cortisol responses, to the
Trier Social Stress Test. Gaussian mixture modeling technique is
employed to automatically cluster individuals based on their personality
traits and electrocardiogram responses. Simultaneously,
individuals are classified based on changes in salivary cortisol levels.
Resulting clusters are labelled based on the literature on stress
recovery. The relationships between personality and physiology
groups are investigated. Reduced stress recovery observed via
salivary cortisol responses is associated with higher neuroticism
and lower extraversion, as well as attenuated electrocardiogram
recovery responses. Higher cortisol reactivity during stress is
found to be positively associated with higher cortisol recovery.
Therefore, the study implies that consideration of personality
traits is likely to aid stress detection and recovery models.
... If the wearable devices are made with more accurate measurements under mechanical stress and strain (tactile sensor) , they may be more suitable for commercial applications. Different kinds of wearable devices are available in the market for regular usage in the form of smart watches , smart contact lens (Rodger et al. 2006), smart bandages (Long et al. 2018), clothing (Lin et al. 2018), etc. ...
By their geometric dimensions, biosensors can be arranged into a sequence of macro-, micro- and nanodevices. From this perspective, the present chapter considers mainly electrochemical biosensors and biofuel cells; attention is also paid to other, e.g. chemical, types of sensors that can be prototypes of developed biosensor devices. One of the moving forces of miniaturization is finding conditions when the least amount of material is used. Miniature devices can play the role of biosensors and represent electrodes for biofuel systems. Due to the broad use of the enzyme glucose oxidase (GOD), which acts as a model protein or the base of existing devices, we discuss in detail the planar technology of forming a multichannel nanobiochip based on the immobilized GOD. The prospects of micro/nanostructures are primarily determined by their miniature size, the feasibility of easy duplication and—in production—a combined application of molecular electronics technology and biochemical methods of manipulations with biological material.
