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Sensor Networks in Healthcare: A New Paradigm for Improving Future Global Health
Published in Daniel Tze Huei Lai, Rezaul Begg, Marimuthu Palaniswami, Healthcare Sensor Networks, 2016
Daniel T.H. Lai, Braveena Santhiranayagam, Rezaul K. Begg, Marimuthu Palaniswami
The success of an HSN solution in the market depends very much on the reliable availability of data from different sources, particularly in this case data that can be obtained from modern sensor network platforms. The constraint, however, is that the critically needed data may be stored in different repositories and locations. Efficient and secure access to these resources can limit on the commercial exploitation capabilities by a variety of organizations. The exact business model of how this is managed needs further consideration. For example, HSN solutions for fall prevention can be wirelessly connected to existing monitoring systems in private homes and residential aged-care facilities. These monitoring systems, such as Australia’s Medi-Link dialer from Smartlink International, GE’s Quiet Care system (US) and Intel Health Guide (US), are part of the rapidly expanding home-based health monitoring market, estimated to grow from US$3 billion (2009) to 7.7 billion (2012) (“GE and Intel to Form Healthcare Alliance,” 2009). One current successful business model in Australia is Harvey Norman, which markets the Medi-Link dialer for US$375 but offers renting and monitoring service options for A$1.08 per day.
Artificial Intelligence for Accurate Detection and Analysis of Freezing of Gait in Parkinson's Disease
Published in Richard Jiang, Li Zhang, Hua-Liang Wei, Danny Crookes, Paul Chazot, Recent Advances in AI-enabled Automated Medical Diagnosis, 2022
Debin Huang, Wenting Yang, Simeng Li, Hantao Li, Lipeng Wang, Wei Zhang, Yuzhu Guo
As the world’s second prevalent neurodegenerative disease, Parkinson’s disease (PD) affects more than ten million people [60] worldwide and this number is expected to double by 2050 [52], especially among the elderly. Because of the loss of dopaminergic [10], PD symptoms are manifested as: slowness of motion, muscle tremor and rigidity, freezing of gait (FoG) and other impaired motor functions [32, 33]. Results of a survey of 6,620 patients with PD showed that about half have the experience of regular gait freeze [48]. FoG commonly occurs in gait initiation, turning, passing through narrow space or approaching obstacles in the patient’s daily life, which significantly increases the risk of falling during walking. As the most serious disability motor symptom of PD, FoG can often have a significant impact on the quality of life of advanced PD patients [44, 83]. The latent pathology of FoG is still unclear; however, there are some common characteristics in the gait, such as sharp decrease in stride length, increase of cadence, and high-frequency leg movements [62]. That FoG often happens suddenly, asymmetrically, and with a short duration [64, 69, 83] makes the clinical detection, tracking, and evaluation of the onset of FoG a challenging task. Freezing of gait in PD is common and debilitating, thus increasing the demands on supportive caregivers’ stress and non-motor illness burden, such as anxiety and depression. Anxiety often occurs during ‘off’ periods; it improves with better control of motor symptoms but can be a major source of distress for patients even during the ‘on’ state. On-going assessment and punctual and proper supportive care becomes increasingly important in advanced PD. Fall prevention is essential to avoid serious fracture or injuries. FoG is often associated with end-stage disease and is typically difficult to handle. With the reduction of the efficacy of medication, non-pharmacologic treatments, such as auditory cueing and visual cueing may eliminate or diminish the freezing episodes [3].
Minimally Invasive Microneedle Sensors Developments in Wearable Healthcare Devices
Published in Suresh Kaushik, Vijay Soni, Efstathia Skotti, Nanosensors for Futuristic Smart and Intelligent Healthcare Systems, 2022
Akshay Krishnakumar, Ganesh Kumar Mani, Raghavv Raghavender Suresh, Arockia Jayalatha Kulandaisamy, Kazuyoshi Tsuchiya, John Bosco Balaguru Rayappan
The key attribute of ‘physical fitness’ proves to be a hub, connecting and regulating several pivotal health aspects such as mental well-being, blood pressure, weight control, immune response, and more. Regular physical activity is reported to improve the stress response and social skills of individuals (The ETO 2006) and boost their cardiovascular performance. Menopause in women leads to an increase in fat content, a decrease in muscle mass, a reduction in bone strength, which subsequently increases the chances of bone fracture and osteoporosis. Thus, regular engagement in physical activities tends to address the pre-mentioned effects associated with menopause and hence improve their overall health (Barbara Sternfeld 2012). In this context, the practice of less demanding exercises such as walking, jogging and swimming and strenuous exercises such as weightlifting and running is seen in a population of all ethnicity and age groups to bolster their physical fitness. In order to reap the best outcomes from physical activities, it is important to neither over-strain nor under-exert oneself during the practice and hence it is imperial to carry out these activities at/for an optimum ‘intensity, duration and frequency’ which is personalized to an individual (Barbara Sternfeld 2012). Ill-effects associated with over/under-exertion of physical activities may be suitably addressed through one’s regular monitoring of their physical fitness defining parameters (Haskell et al. 2012), such as aerobic capacity, muscle strength/exertion, stamina, flexibility and mobility of joints, shoulders and more. Subsequently, wearable sensors have capitalized on this niche environment towards continuous monitoring of one’s physical performance which has attracted significant welcoming in the domain of sports and rehabilitation. On the other hand, neurodegenerative disorders such as Alzheimer’s (AD) and Parkinson’s disease (PD), which results in impaired motor functions in an individual, makes it exceedingly difficult for them to exhibit simple locomotion and other volitional activities (Kluger et al. 1997, Grabli et al. 2012, Chen et al. 2013). This subsequently renders them to greater risks of falling or to some other kind of physical injury, while walking or performing other simple tasks. In this context, the use of wearable sensors towards fall detection or movement monitoring in such disease afflicted patients and the corresponding development of fall prevention technologies have garnered significant attention. Wearable sensors designed for the pre-mentioned purposes are reported in this sub-section.
Predicting The Risk of Fall in Community-Dwelling Older Adults in Iran
Published in Journal of Aging and Environment, 2023
Sahar Keyvanloo Shahrestanaki, Farshad Sharifi, Hooman Shahsavari, Fatemeh Ghonoodi, Ian Philp, Fatemeh Bahramnezhad, Elham Navab
Falls are one of the most important causes of disability and mortality in community-dwelling older people (Ambrose et al., 2013; Verma et al., 2016). The cause of fall in older adults is multifactorial. The major risk factors of fall are polypharmacy, a history of previous fall, an impaired gait and balance, frailty and muscle weakness, and other risk factors, such as advancing age, female gender, dizziness and vertigo, cognitive status, Postural hypotension, visual disorder, syncope, and environmental factors (Ambrose et al., 2013; Deandrea et al., 2010). Recognition of older adults who are at the risk of falling could help health providers and family caregivers to plan for fall prevention (Clemson et al., 2004; Phelan et al., 2015). Several studies have shown that interventions, such as physical training (Skelton et al., 2005) and multidimensional interventions could decrease the number of falls among older adults living in the community (Kannus et al., 2005; Nematollahi et al., 2016).
Wearable inertial sensors for human movement analysis: a five-year update
Published in Expert Review of Medical Devices, 2021
Pietro Picerno, Marco Iosa, Clive D’Souza, Maria Grazia Benedetti, Stefano Paolucci, Giovanni Morone
Falls in older adults represent a major healthcare problem with a third of all older adults over 65 years old estimated to experience a fall at least once per year [76]. The frequency of falls increases with age and frailty and with increasing prevalence given the ongoing aging of the population [77]. Falls significantly affect the lives of older adults leading to a decreased quality of life and increased morbidity and mortality [78]. Consequences of falls also result in severe public health care costs from hospitalization and rehabilitation [79]. For these reasons, fall prevention and detection strategies are crucial for reducing the risk of future falls and mitigating their health-related consequences [78,80]. While falls occur unexpectedly during daily-life activities, the risk of fall events can be predicted from gait disorders [76]. One effective approach to developing fall prevention and detection strategies is through unsupervised continuous monitoring of daily-life activities, with wearable IMUs presenting the most suitable and straightforward solution to adopt. The body of literature related to falls and wearable IMUs can be divided into four areas of active research and clinical impact: fall risk classification, near-fall, pre-fall and fall detection.