A demand for wearable devices in health care

Vijayalakshmi, K.; Uma, S.; Bhuvanya, R.; Suresh, A.

doi:10.14419/ijet.v7i1.7.9377

Cited by 26 publications

(14 citation statements)

References 5 publications

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“…As the focus of this study is consumers of wearable health device, the unit of analysis is individual. e questionnaire item was adopted from the previous literature [2,3,6,20,51] and supposed to be suitable in this study. Twenty-eight items were used to measure the seven variables.…”

Section: Measurement Of Variablesmentioning

confidence: 99%

“…is means that remote monitoring of physical data and health status is feasible. Remote assessment of patients' health status might also remarkably decline medical expenses by reducing the number of unnecessary patient visits to medical facilities [3,4]. e wearable health devices not only are considered as hardware but also discover users' important features by using mobile application and web software.…”

Section: Introductionmentioning

confidence: 99%

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An Integrated SEM-Neural Network Approach for Predicting Determinants of Adoption of Wearable Healthcare Devices

Asadi

Abdullah

Safaei

et al. 2019

Mobile Information Systems

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The advancement in wireless sensor and information technology has offered enormous healthcare opportunities for wearable healthcare devices and has changed the way of health monitoring. Despite the importance of this technology, limited studies have paid attention for predicting individuals’ influential factors for adoption of wearable healthcare devices. The proposed research aimed at determining the key factors which impact an individual's intention for adopting wearable healthcare devices. The extended technology acceptance model with several external variables was incorporated to propose the research model. A multi-analytical approach, structural equation modelling-neural network, was considered for testing the proposed model. The results obtained from the structural equation modelling showed that the initial trust is considered as the most determinant and influencing factor in the decision of wearable health device adoption followed by health interest, consumer innovativeness, and so on. Moreover, the results obtained from the structural equation modelling applied as an input to the neural network indicated that the perceived ease of use is one of the predictors that are significant for adoption of wearable health devices by consumers. The proposed study explains the wearable health device implementation along with test adoption model, and their outcome will help providers in the manufacturing unit for increasing actual users’ continuous adoption intention and potential users’ intention to use wearable devices.

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Section: Measurement Of Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Integrated SEM-Neural Network Approach for Predicting Determinants of Adoption of Wearable Healthcare Devices

Asadi

Abdullah

Safaei

et al. 2019

Mobile Information Systems

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“…One type of technological device that is presently having a large impact is the wearable device (wearable technology). According to Vijayalakshmi et al [1], in 2018, approximately 210 million units of wearable devices were produced, amounting revenues of more than USD 30 billion.…”

Section: Introductionmentioning

confidence: 99%

Empirical Study Based on the Perceptions of Patients and Relatives about the Acceptance of Wearable Devices to Improve Their Health and Prevent Possible Diseases

Guillén‐Gámez

Mayorga-Fernández

2019

Mobile Information Systems

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In recent years, there has been a technological revolution affecting all areas of life, including health. Following this trend, a series of electronic devices called wearable technology have emerged with the aim of improving the quality of people’s life. These devices are made up of tools that help to prevent the development of chronic diseases and fight against the aging of society by monitoring the vital signs of users. Despite the advantages of using these devices from medical and health points of view, it is necessary to know the opinion of users regarding their use. For this reason, this study has been proposed. The purpose of this study is to analyse the perceptions of the subjects involved in the research regarding the acceptance of wearable devices, taking into account variables, such as age and gender. To this end, a nonexperimental, ex post facto design has been composed that combines descriptive and inferential techniques, with a sample of 606 patients and relatives belonging to a public health centre in the community of Madrid.

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“…The advances in electronics and, in particular, in microelectronics, together with the emergence of new wireless communication protocols have been contributing to the successful development of wearable devices [ 1 , 2 ]. These can be defined as “devices that can be worn or mated with human skin, to continuously and closely monitor an individual’s activities, without interrupting or limiting the user’s motion” [ 3 ].…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…Wearable Health Devices, as they are known, have been used for health and wellness monitoring, home rehabilitation, treatment efficiency assessment and early detection of disorders [ 1 ]. All these categories imply the use of wearable devices for monitoring human physiological signals, such as electrocardiogram, heart rate, blood pressure, respiratory rate, body temperature and motion [ 5 ].…”

Section: Introduction and Related Workmentioning

confidence: 99%

SnapKi—An Inertial Easy-to-Adapt Wearable Textile Device for Movement Quantification of Neurological Patients

Oliveira

Dias

Lopes

et al. 2020

Sensors

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The development of wearable health systems has been the focus of many researchers who aim to find solutions in healthcare. Additionally, the large potential of textiles to integrate electronics, together with the comfort and usability they provide, has contributed to the development of smart garments in this area. In the field of neurological disorders with motor impairment, clinicians look for wearable devices that may provide quantification of movement symptoms. Neurological disorders affect different motion abilities thus requiring different needs in movement quantification. With this background we designed and developed an inertial textile-embedded wearable device that is adaptable to different movement-disorders quantification requirements. This adaptative device is composed of a low-power 9-axis inertial unit, a customised textile band and a web and Android cross application used for data collection, debug and calibration. The textile band comprises a snap buttons system that allows the attachment of the inertial unit, as well as its connection with the analog sensors through conductive textile. The resulting system is easily adaptable for quantification of multiple motor symptoms in different parts of the body, such as rigidity, tremor and bradykinesia assessments, gait analysis, among others. In our project, the system was applied for a specific use-case of wrist rigidity quantification during Deep Brain Stimulation surgeries, showing its high versatility and receiving very positive feedback from patients and doctors.

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A demand for wearable devices in health care

Cited by 26 publications

References 5 publications

An Integrated SEM-Neural Network Approach for Predicting Determinants of Adoption of Wearable Healthcare Devices

An Integrated SEM-Neural Network Approach for Predicting Determinants of Adoption of Wearable Healthcare Devices

Empirical Study Based on the Perceptions of Patients and Relatives about the Acceptance of Wearable Devices to Improve Their Health and Prevent Possible Diseases

SnapKi—An Inertial Easy-to-Adapt Wearable Textile Device for Movement Quantification of Neurological Patients

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