Ambient intelligence for long-term diabetes care (AmILCare). Qualitative analysis of patients’ expectations and attitudes toward interactive technology

Trento, Marina; Franceschini, Marta; Fornengo, Paolo; Tricarico, Lucia; Mazzeo, Aurora; Bertello, Stefania; Clerico, Alessandra; Oleandri, S. E.; Chiesa, Mario; Leva, Anna Di; Charrier, Lorena; Cavallo, Franco; Porta, Massimo

doi:10.1007/s12020-021-02694-1

Cited by 2 publications

(2 citation statements)

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“…9 Recently, we reported that people with noninsulin-treated type 2 diabetes will accept technological support if noninvasive and maintaining confidentiality. 10 Whilst the wrist may seem more natural and comfortable (the GT9X doubles into a digital timepiece), our results, despite the small sample, strongly suggest that accelerometers are best worn at the waist to collect plausible data and provide reliable feedback and guidance, if patients are to benefit from appropriate lifestyle adjustment. T A B L E 1 (Continued)…”

Section: Lettermentioning

confidence: 75%

Detection of real‐life activities by a tri‐axial accelerometer worn at different body locations: Analysis and interpretation

et al. 2021

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Section: Lettermentioning

confidence: 75%

Detection of real‐life activities by a tri‐axial accelerometer worn at different body locations: Analysis and interpretation

et al. 2021

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“…Covid-19 is very contagious, and we are living in an era of ambient intelligence [5]. Ambient intelligence has made a huge impact in smart healthcare systems to give people a life outside the hospitals [6]. Healthcare improves health by diagnosing the disease, preventing it, treating it, and other mental or physical illnesses and injuries.…”

Section: Introductionmentioning

confidence: 99%

Situation-Aware BDI Reasoning to Detect Early Symptoms of Covid 19 Using Smartwatch

Saleem

Zeeshan

et al. 2023

IEEE Sensors J.

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Ambient intelligence plays a crucial role in healthcare situations. It provides a certain way to deal with emergencies to provide the essential resources such as nearest hospitals and emergency stations promptly to avoid deaths. Since the outbreak of Covid-19, several artificial intelligence techniques have been used. However, situation awareness is a key aspect to handling any pandemic situation. The situation-awareness approach gives patients a routine life where they are continuously monitored by caregivers through wearable sensors and alert the practitioners in case of any patient emergency. Therefore, in this paper, we propose a situation-aware mechanism to detect Covid-19 systems early and alert the user to be self-aware regarding the situation to take precautions if the situation seems unlikely to be normal. We provide Belief-Desire-Intention intelligent reasoning mechanism for the system to analyze the situation after acquiring the data from the wearable sensors and alert the user according to their environment. We use the case study for further demonstration of our proposed framework. We model the proposed system by temporal logic and map the system illustration into a simulation tool called NetLogo to determine the results of the proposed system.

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Ambient intelligence for long-term diabetes care (AmILCare). Qualitative analysis of patients’ expectations and attitudes toward interactive technology

Cited by 2 publications

References 19 publications

Detection of real‐life activities by a tri‐axial accelerometer worn at different body locations: Analysis and interpretation

Detection of real‐life activities by a tri‐axial accelerometer worn at different body locations: Analysis and interpretation

Situation-Aware BDI Reasoning to Detect Early Symptoms of Covid 19 Using Smartwatch

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