Pathway of Trends and Technologies in Fall Detection: A Systematic Review

Tanwar, Rohit; Nandal, Neha; Zamani, Mazdak; Manaf, Azizah Abdul

doi:10.3390/healthcare10010172

Cited by 45 publications

(25 citation statements)

References 106 publications

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“…COVID-19 has proven that cameras can be crucial in detecting face masks [ 75 ], aiding both healthcare and law enforcement agencies. One of the low-cost approaches to fall detection, according to the review in [ 76 ], is the use of camera-based devices. A future direction we see developing at least in testing, after taking into consideration privacy concerns and anonymizing the data, consists of surveillance nodes sending information to processing servers where fall-detection events could be confirmed and acted on to the extent of notifying healthcare services.…”

Section: Discussionmentioning

confidence: 99%

A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence

Pap

Oniga

2022

IJERPH

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Over the last couple of years, in the context of the COVID-19 pandemic, many healthcare issues have been exacerbated, highlighting the paramount need to provide both reliable and affordable health services to remote locations by using the latest technologies such as video conferencing, data management, the secure transfer of patient information, and efficient data analysis tools such as machine learning algorithms. In the constant struggle to offer healthcare to everyone, many modern technologies find applicability in eHealth, mHealth, telehealth or telemedicine. Through this paper, we attempt to render an overview of what different technologies are used in certain healthcare applications, ranging from remote patient monitoring in the field of cardio-oncology to analyzing EEG signals through machine learning for the prediction of seizures, focusing on the role of artificial intelligence in eHealth.

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Section: Discussionmentioning

confidence: 99%

A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence

Pap

Oniga

2022

IJERPH

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“…The community has already started to set the foundations and the principles of such transition. We refer the interested reader to the excellent recent reviews [126][127][128][129]. In summary, the fusion of wearable and ambient sensors and a hybrid approach of proper education, IoT techniques, and clinical support is expected to affect the fall prediction and detection systems positively.…”

Section: Perspectivesmentioning

confidence: 99%

Preventing falls: the use of machine learning for the prediction of future falls in individuals without history of fall

et al. 2022

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Nowadays, it becomes of paramount societal importance to support many frail-prone groups in our society (elderly, patients with neurodegenerative diseases, etc.) to remain socially and physically active, maintain their quality of life, and avoid their loss of autonomy. Once older people enter the prefrail stage, they are already likely to experience falls whose consequences may accelerate the deterioration of their quality of life (injuries, fear of falling, reduction of physical activity). In that context, detecting frailty and high risk of fall at an early stage is the first line of defense against the detrimental consequences of fall. The second line of defense would be to develop original protocols to detect future fallers before any fall occur. This paper briefly summarizes the current advancements and perspectives that may arise from the combination of affordable and easy-to-use non-wearable systems (force platforms, 3D tracking motion systems), wearable systems (accelerometers, gyroscopes, inertial measurement units-IMUs) with appropriate machine learning analytics, as well as the efforts to address these challenges.

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“…These systems continuously monitor older individuals using devices to find the fall prediction. These can be divided into three categories 47 , 48 :…”

Section: Technology In Fall Detection and Preventionmentioning

confidence: 99%

Falls in older adults: a practical approach

Pereira

Kanashiro

2022

Arq. Neuro-Psiquiatr.

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Background: Falls are a major problem in public health since they are an important cause of morbidity and mortality. To evaluate the risk of fall and prescribe preventive interventions may be a challenging task. Objectives: The objectives of this study are to summarize the most relevant information on the topic “falls in the elderly” and to give a critical view and practical clinical approach on this topic. Methods: In March 2022, a search of Pubmed database was performed, using the terms “fall elderly”, fall prevention”, “fall risk”, with the following parameters: five years, review, systematic review, meta-analysis, practice guidelines. Results: There are several risk factors for falls that can be grouped in different areas (psychosocial, demographic, medical, medication, behavioral, environmental). The clinical evaluation of an older adult prone to falls must include identification of risk factors through history and examination and identification of risk of falls through an assessment tool such as gait velocity, functional reach test, timed up and go, Berg balance test, and miniBEST test. Fall prevention strategies can be single or multiple, and physical activity is the most cited. Technology can be used to detect and prevent falls. Conclusion: A systematic approach to the older patient in risk of falls is feasible and may impact fall prevention.

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Pathway of Trends and Technologies in Fall Detection: A Systematic Review

Cited by 45 publications

References 106 publications

A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence

A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence

Preventing falls: the use of machine learning for the prediction of future falls in individuals without history of fall

Falls in older adults: a practical approach

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