Assistive Technology and Older Adults

Investigating the Performance of Wearable Motion Sensors on recognizing falls and daily activities via machine learning

“…However, SVM has been developed to make nonlinear classifications by using the kernel method. In this study, the SVM model was tested with three different hyperparameters, namely, the specific kernel types used in the algorithm ('linear', 'poly', 'rbf', and 'sigmoid'), the value of the regulation parameter C (0.1, 0.3, 0.5, 0.7, 0.9, 1.0, 1.3, 1.5, 1.7, 2.0), and the degree of the polynomial kernel function 'poly' (2,3,4,5). SVM model shows the best performance for binary and multi-class activity recognition with C = 1, 'linear' kernel type with sensor type combination AM, and C = 0.1, 'linear' kernel type with sensor type combination AGM, respectively.…”

“…It is estimated that this ratio will reach nearly 12% (1 billion) in 2030 and 16% (1.6 billion) in 2050 [2]. In this context, developing assistive technologies to support the daily lives of elderly and disabled people, increase their safety and autonomy, detect potentially dangerous events such as falls reliably have become important and challenging research issues [3]. In addition to detecting fall events reliably, research has focused on monitoring and recognizing ADLs to improve the quality-of-life of people in the fall risk groups.…”

Views and Experiences of People with Intellectual Disabilities to Improve Access to Assistive Technology: Perspectives from India

“…Intellectual disability is defined by the American Association on Intellectual and Developmental Disabilities (AAIDD), the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) and the International Classification of Diseases (ICD-10, mental retardation) as an IQ below 70, manifested during the developmental period (onset before 18 years of age), with impairments in adaptive functioning, such as communication skills, social skills, personal independence, school or work functioning (AAIDD, 2013;American Psychiatric Association, 2013;WHO, 2016). It has been found that people with intellectual disabilities use fewer ATs compared to other people in need (Wehmeyer, 1995;Carey et al, 2005, Kaye et al, 2008Hatton and Emerson, 2015), despite the fact that people with intellectual disabilities could greatly benefit from AT (Patja et al, 2000;Haveman et al, 2011;Hatton and Emerson, 2015;Carmeli et al, 2016;Owuor et al, 2017). The benefits that relate to AT are that it (1) could be used to support cognitive limitations in order to enhance independence and inclusion, (2) could facilitate better management of chronic health conditions and comorbidities which people with intellectual disabilities experience more often compared to the general population, such as sensory impairments, speech and language impairments, and dementia ( Jansen and Kingma-Thijsen, 2011;Hatton and Emerson, 2015), and (3) could support those with early onset of functional decline ( Haveman et al, 2011;Schoufour et al, 2015).…”