A Novel IoT Based Positioning and Shadowing System for Dementia Training

Abstract

A rapid increase in the number of patients with dementia, particularly memory decline or impairment, has led to the loss of self-care ability in more individuals and increases in medical and social costs. Numerous studies, and clinical service experience, have revealed that the intervention of nonpharmacological management for people with dementia is effective in delaying the degeneration caused by dementia. Due to recent rapid developments in information and communications technology, many innovative research and development and cross-domain applications have been effectively used in the dementia care environment. This study proposed a new short-term memory support and cognitive training application technology, a “positioning and shadowing system,” to delay short-term memory degeneration in dementia. Training courses that integrate physical and digital technologies for the indoor location of patients with dementia were constructed using technologies such as Bluetooth Low Energy, fingerprint location algorithm, and short-range wireless communication. The Internet of Things was effectively applied to a clinical training environment for short-term memory. A pilot test verified that the results demonstrated learning effects in cognitive training and that the system can assist medical personnel in training and nursing work. Participants responded with favorable feedback regarding course satisfaction and system usability. This study can be used as a reference for future digital smart cognitive training that allows observation of the performance of patients with dementia in activities of daily living.

Defining and measuring the effects of digital technologies on social sustainability: A systematic literature review

“…As explained in Section 3.2.4, direct experimentation is not very frequent, and most of the data used in the literature comes from surveys, interviews or case studies. In fact, we can find several calls for more empirical studies in the reviewed papers (e.g., Affolderbach & de Chardon, 2021; Ekener, 2019; Hervani et al, 2022; Hung et al, 2021; Palumbo et al, 2022; Tavares & Azevedo, 2022; Young et al, 2019), with specific examples such as “there is a need for more empirical studies and quantitative research” (in the context of Industry 4.0 and Sustainability) (Mastrocinque et al, 2022) or the remark from (Grybauskas et al, 2022) about the “sheer lack of empirical evidence on the positive or negative impacts of Industry 4.0 on social sustainability”.…”

In-Home Positioning for Remote Home Health Monitoring in Older Adults: Systematic Review (Preprint)

“…Most studies were done in the home setting (7/10, 70%), with two in a home-like lab setting and one in a lab setting. Studies in the lab-home involved monitoring sessions lasting between 1 hour and 7 days (Hung et al, 2021;Rahal et al, 2008;Tegou et al, 2019), while home-based monitoring ranged from 3 weeks to 18 months. Table 4 displays the outcomes from studies that included patient populations.…”

“…Room detection accuracy ranged from 50-88% across three studies (Lach et al, 2019;Montoliu et al, 2020;Rahal et al, 2008), while one study reported failure rates of >15% for motion detectors and servers installed in the home (Hu et al, 2016). One study reported linear accuracies of 1.5-2.0m using wireless sensor networks within the home (Hung et al, 2021).…”

“…For studies that included usability and acceptability outcomes, surveys from three studies (Hu et al, 2016;Hung et al, 2021;Pais et al, 2020) showed positive results. One study focused on ease of setup of a smart home in a box design and found high ease of use, few concerns with devices, and highly efficient instructions (Hu et al, 2016).…”

“…Ambient monitors, including infrared sensors and magnetic door contact sensors, can detect which room a resident is in to provide some context of the activities performed (Kaye et al, 2018;Lach et al, 2019;Rahal et al, 2008). Wearable tags using wireless technologies like Bluetooth or Wifi, can also be used to localize residents in their homes, offering 1.5-5m accuracy (Hung et al, 2021;Montoliu et al, 2020).…”