Smart aging : utilisation of machine learning and the Internet of Things for independent living

Smart aging utilises innovative approaches and technology to improve older adults’ quality of life, increasing their prospects of living independently. One of the major concerns the older adults to live independently is “serious fall”, as almost a third of people aged over 65 having a fall each year. Dementia, affecting nearly 9% of the same age group, poses another significant issue that needs to be identified as early as possible. Existing fall detection systems from the wearable sensors generate many false alarms; hence, a more accurate and secure system is necessary. Furthermore, there is a considerable gap to identify the onset of cognitive impairment using remote monitoring for self-assisted seniors living in their residences. Applying biometric security improves older adults’ confidence in using IoT and makes it easier for them to benefit from smart aging. Several publicly available datasets are pre-processed to extract distinctive features to address fall detection shortcomings, identify the onset of dementia system, and enable biometric security to wearable sensors. These key features are used with novel machine learning algorithms to train models for the fall detection system, identifying the onset of dementia system, and biometric authentication system. Applying a quantitative approach, these models are tested and analysed from the test dataset. The fall detection approach proposed in this work, in multimodal mode, can achieve an accuracy of 99% to detect a fall. Additionally, using 13 selected features, a system for detecting early signs of dementia is developed. This system has achieved an accuracy rate of 93% to identify a cognitive decline in the older adult, using only some selected aspects of their daily activities. Furthermore, the ML-based biometric authentication system uses physiological signals, such as ECG and Photoplethysmogram, in a fusion mode to identify and authenticate a person, resulting in enhancement of their privacy and security in a smart aging environment. The benefits offered by the fall detection system, early detection and identifying the signs of dementia, and the biometric authentication system, can improve the quality of life for the seniors who prefer to live independently or by themselves

Randomised controlled trial of a complex intervention by primary care nurses to increase walking in patients aged 60-74 years: protocol of the PACE-Lift (Pedometer Accelerometer Consultation Evaluation - Lift) trial.

BACKGROUND: Physical activity is essential for older peoples' physical and mental health and for maintaining independence. Guidelines recommend at least 150 minutes weekly, of at least moderate intensity physical activity, with activity on most days. Older people's most common physical activity is walking, light intensity if strolling, moderate if brisker. Less than 20% of United Kingdom 65-74 year olds report achieving the guidelines, despite most being able to. Effective behaviour change techniques include strategies such as goal setting, self-monitoring, building self-efficacy and relapse prevention. Primary care physical activity consultations allow individual tailoring of advice. Pedometers measure step-counts and accelerometers measure physical activity intensity. This protocol describes an innovative intervention to increase walking in older people, incorporating pedometer and accelerometer feedback within a primary care nurse physical activity consultation, using behaviour change techniques. DESIGN: Randomised controlled trial with intervention and control (usual care) arms plus process and qualitative evaluations. PARTICIPANTS: 300 people aged 60-74 years registered with 3 general practices within Oxfordshire and Berkshire West primary care trusts, able to walk outside and with no restrictions to increasing their physical activity. INTERVENTION: 3 month pedometer and accelerometer based intervention supported by practice nurse physical activity consultations. Four consultations based on behaviour change techniques, physical activity diary, pedometer average daily steps and accelerometer feedback on physical activity intensity. Individual physical activity plans based on increasing walking and other existing physical activity will be produced. OUTCOMES: Change in average daily steps (primary outcome) and average time spent in at least moderate intensity physical activity weekly (secondary outcome) at 3 months and 12 months, assessed by accelerometry. Other outcomes include quality of life, mood, exercise self-efficacy, injuries. Qualitative evaluations will explore reasons for trial non-participation, the intervention's acceptability to patients and nurses and factors enhancing or acting as barriers for older people in increasing their physical activity levels. DISCUSSION: The PACE-Lift trial will determine the feasibility and efficacy of an intervention for increasing physical activity among older primary care patients. Steps taken to minimise bias and the challenges anticipated will be discussed. Word count 341. TRIAL REGISTRATION NUMBER: ISRCTN42122561.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are