Biomedical Engineering and Elderly Support

Abstract

The undoubted growing percentage of senior population (age 65+) has been obvious in the modern society during the last few decades. Ambient Assisted Living (AAL) technologies arise as a promising means of elderly care, thereby reducing both carers’ workload and public health services costs. LongLastingMemories, an EU funded project, aimed at alleviating senior people mental and physical health problems by integrating state of the art computer-aided technology and prolonging their independent living by providing a smart home solution. Five rounds of multicentric pilots were planned and conducted in order to test that objectives such as accessibility, user acceptance and perceived effectiveness of the service were met by the final LLM prototype. A questionnaire survey was conducted after each pilot with several stakeholders, such as senior subjects and formal careers, proving the wide acceptance of our service and its applicability in the domain of elderly healthcare.

Gains in cognition through combined cognitive and physical training: the role of training dosage and severity of neurocognitive disorder

“…Post-test was conducted within 2 weeks after completion of the training period. The interventions reported in this paper were carried out in Athens and Thessaloniki (Greece) within day care centers, hospitals, senior care centers, a memory outpatient center, local parishes, at university campus facilities (university community installations), and at participant’s homes ( Bamidis, 2012 ; Billis et al, 2013 ).…”

Cognitive emotion model for eldercare robot in smart home

“…Billis [8] propose a method can change the smart home environment by tracking the human physiological signals to regulate their emotion, and making users into a more efficient working condition; Yu -Chun Huang [9] put forward "Time home pub" can not only adjust the atmosphere of smart home environment according to the user's daily activities, also can promote the emotional communication between the user and smart home. Therefore, under the precondition of facial emotional feature extraction, this paper proposes a cognitive emotional model of eldercare robot in AVS (arousal-valence-stance) emotional space, as well as analyzes the optimization of smart home for this model using simulated annealing algorithm.…”

Smart home technology: Challenges and opportunities for collaborative governance and policy research

“…SHT offers significant potential for improved patient outcomes, particularly as remote healthcare solutions have emerged as a preferred alternative to nursing home care and long‐term hospitalization (Bodenheimer et al, 2002). The integration of mobile health (mHealth) and ambient assisted living (AAL) technologies into smart home environments enables timely preventative care, relieves caregiver burden, reduces patient visits to medical facilities, facilitates safer patient transitions from hospitals to home, and improves patient experiences (Billis et al, 2013; Jusob et al, 2017). SHT facilitates preventative care as motion‐sensing technologies generate context‐aware healthcare systems that continuously monitor biometrics and send alerts to caregivers and healthcare providers as needed (Chiang & Liang, 2015).…”