A Systematic Review of Wearable Sensors and IoT-Based Monitoring Applications for Older Adults – a Focus on Ageing Population and Independent Living

Baig, Mirza Mansoor; Afifi, Shereen; GholamHosseini, Hamid; Mirza, Farhaan

doi:10.1007/s10916-019-1365-7

Cited by 161 publications

(115 citation statements)

References 56 publications

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“…With the ever-growing use of smart systems, end-user acceptability is becoming an essential aspect of the design of such systems. 3,37,38 The user-awareness, as well as clinician and patient acceptance, are key factors for accepting smart systems in healthcare. Data interoperability is one of the main drawbacks of deployed solutions/systems where users are "constrained" within one application for a task and move between the applications to view/review the patient information.…”

Section: Discussionmentioning

confidence: 99%

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Early Detection of Prediabetes and T2DM Using Wearable Sensors and Internet-of-Things-Based Monitoring Applications

et al. 2021

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Background Prediabetes and type 2 diabetes mellitus (T2DM) are one of the major long-term health conditions affecting global healthcare delivery. One of the few effective approaches is to actively manage diabetes via a healthy and active lifestyle. Objectives This research is focused on early detection of prediabetes and T2DM using wearable technology and Internet-of-Things-based monitoring applications. Methods We developed an artificial intelligence model based on adaptive neuro-fuzzy inference to detect prediabetes and T2DM via individualized monitoring. The key contributing factors to the proposed model include heart rate, heart rate variability, breathing rate, breathing volume, and activity data (steps, cadence, and calories). The data was collected using an advanced wearable body vest and combined with manual recordings of blood glucose, height, weight, age, and sex. The model analyzed the data alongside a clinical knowledgebase. Fuzzy rules were used to establish baseline values via existing interventions, clinical guidelines, and protocols. Results The proposed model was tested and validated using Kappa analysis and achieved an overall agreement of 91%. Conclusion We also present a 2-year follow-up observation from the prediction results of the original model. Moreover, the diabetic profile of a participant using M-health applications and a wearable vest (smart shirt) improved when compared to the traditional/routine practice.

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Section: Discussionmentioning

confidence: 99%

“…Wearable monitoring applications demonstrated a positive impact on the individual's overall health and wellbeing when used for long-term/chronic care or continuous monitoring. [1][2][3][4]…”

Section: Background and Significancementioning

confidence: 99%

Early Detection of Prediabetes and T2DM Using Wearable Sensors and Internet-of-Things-Based Monitoring Applications

et al. 2021

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“…As may be seen, various technological solutions reveal to be potentially interesting tools to support independent living at home, and the recent results for the prevention of falls are promising. Nevertheless, challenges and barriers to the wider adoption of WS and IoT application still exist [18]. Today, few studies address the feasibility and acceptability of these devices outside a laboratory context, to detect the activities of an elderly person's daily routine, which is the next logical step to take [19].…”

Section: Related Work 21 It For Support Of Independent Livingmentioning

confidence: 99%

Detection of Semantic Risk Situations in Lifelog Data for Improving Life of Frail People

Yebda

Benois‐Pineau

Pech

et al. 2020

Proceedings of the 2020 International Conference on Multimedia Retrieval

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The automatic recognition of risk situations for frail people is an urgent research topic for the interdisciplinary artificial intelligence and multimedia community. Risky situations can be recognized from lifelog data recorded with wearable devices. In this paper, we present a new approach for the detection of semantic risk situations for frail people in lifelog data. Concept matching between general lifelog and risk taxonomies was realized and tuned AlexNet was deployed for detection of two semantic risks situations such as risk of domestic accident and risk of fraud with promising results. CCS CONCEPTS • Applied computing → Health care information systems; • Computing methodologies → Neural networks.

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“…Mansoor et al [ 33 ] performed a review on wearable sensors for older adults. The review focused on sensor target population, sensor type, application area, data processing, and usability.…”

Section: Introductionmentioning

confidence: 99%

Sensing Systems for Respiration Monitoring: A Technical Systematic Review

Vanegas

Igual

Plaza

2020

Sensors

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Respiratory monitoring is essential in sleep studies, sport training, patient monitoring, or health at work, among other applications. This paper presents a comprehensive systematic review of respiration sensing systems. After several systematic searches in scientific repositories, the 198 most relevant papers in this field were analyzed in detail. Different items were examined: sensing technique and sensor, respiration parameter, sensor location and size, general system setup, communication protocol, processing station, energy autonomy and power consumption, sensor validation, processing algorithm, performance evaluation, and analysis software. As a result, several trends and the remaining research challenges of respiration sensors were identified. Long-term evaluations and usability tests should be performed. Researchers designed custom experiments to validate the sensing systems, making it difficult to compare results. Therefore, another challenge is to have a common validation framework to fairly compare sensor performance. The implementation of energy-saving strategies, the incorporation of energy harvesting techniques, the calculation of volume parameters of breathing, or the effective integration of respiration sensors into clothing are other remaining research efforts. Addressing these and other challenges outlined in the paper is a required step to obtain a feasible, robust, affordable, and unobtrusive respiration sensing system.

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A Systematic Review of Wearable Sensors and IoT-Based Monitoring Applications for Older Adults – a Focus on Ageing Population and Independent Living

Cited by 161 publications

References 56 publications

Early Detection of Prediabetes and T2DM Using Wearable Sensors and Internet-of-Things-Based Monitoring Applications

Early Detection of Prediabetes and T2DM Using Wearable Sensors and Internet-of-Things-Based Monitoring Applications

Detection of Semantic Risk Situations in Lifelog Data for Improving Life of Frail People

Sensing Systems for Respiration Monitoring: A Technical Systematic Review

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