Classification of Daily Life Activities for Human Fall Detection: A Systematic Review of the Techniques and Approaches

2020 ◽  
pp. 137-179 ◽  
Author(s):  
Yoosuf Nizam ◽  
M. Mahadi Abdul Jamil
Keyword(s):  
Systematic Review ◽  
Daily Life ◽  
Fall Detection ◽  
Daily Life Activities

scholarly journals Elderly Fall Detection using Lightweight Convolution Deep Learning Model

2021 ◽  
Vol 12 (2) ◽  
pp. 2097-2103
Author(s):  
Neeraj Varshney
Keyword(s):  
Deep Learning ◽  
Daily Life ◽  
Fall Detection ◽  
Living Alone ◽  
Sensor Data ◽  
Routine Activity ◽  
Old People ◽  
Accurate Identification ◽  
Daily Life Activities ◽  
Deep Learning Model

Old people, who are living alone at home face serious problem of Falls while moving from one place to another and sometime life threading also. In order to prevent this situation, several fall monitoring systems based on sensor data were proposed. However, there was an issue of misclassification to identify the fall as daily life activities and also routine activity as fall. Towards this end, a deep learning based model is proposed in this paper by using the data of heart rate, BP and sugar level to identify fall along with other daily life activities like walking, running jogging etc. For accurate identification of fall accidents, a publicly accessible data collection and a lightly weighted CNN model are used. The model reports proposed and 98.21 % precision.

scholarly journals Classification of Fall Detection System for Elderly: Systematic Review

2021 ◽  
Vol 12 (3) ◽  
pp. 1769-1780
Author(s):  
Ainul Husna Mohd Yusoff Et.al
Keyword(s):  
Systematic Review ◽  
Health Care ◽  
Detection System ◽  
Fall Detection ◽  
The Elderly ◽  
Living Environment ◽  
Depth Information ◽  
Extensive Literature ◽  
Detection Systems

Elderly are the world’s largest growing population, categorized over the age of 60 to 65 years. They are the ones who prone to fall due to their old age and low self-efficacy, thus making them vulnerable to different accidents. Even doing daily activities can also expose the elderly to a fall incident. As a result, it has gained the attention of many researchers in conducting studies related to the elderly daily health care, especially in relation to the fall detection system. This paper aims to provide a systematic review on the classification of fall detection systems for the elderly. This systematic review is designed based on the existing and extensive literature review on fall detection systems guided by the prisma statement (preferred reporting items for systematic reviews and meta-analyses) review method. Based on this systematic review, four overarching themes that provide in-depth information on fall detection to detect fall events have been identified; classification of fall detection, basis development, type of sensor and detection technique. In a nutshell, the fall detection approach has successfully provided an alternative health care services for elderly who choose to live independently. Therefore, it is important to continue to develop a fall detection system that integrates with technology in order to provide a safe living environment for elderly, and for children, it can offer as an alternative for monitoring systems.

scholarly journals Perampanel Improves Cortical Myoclonus and Disability in Progressive Myoclonic Epilepsies: A Case Series and a Systematic Review of the Literature

2021 ◽  
Vol 12 ◽  
Author(s):  
Giovanni Assenza ◽  
Cristofaro Nocerino ◽  
Mario Tombini ◽  
Giancarlo Di Gennaro ◽  
Alfredo D'Aniello ◽  
...  
Keyword(s):  
Systematic Review ◽  
Daily Life ◽  
Scientific Evidence ◽  
Genetic Diagnosis ◽  
Case Series ◽  
Walking Ability ◽  
Lafora Disease ◽  
Daily Life Activities ◽  
Action Myoclonus ◽  
Prospective Trials

Introduction: Progressive myoclonic epilepsies (PMEs) are a heterogenous group of genetic diseases presenting with epilepsy, cognitive impairment, and severe action myoclonus, which can severely affect daily life activities and independent walking ability. Perampanel is a recent commercially available antiseizure medication with high efficacy against generalized seizures. Some reports supported the role of perampanel in ameliorating action myoclonus in PMEs. Here, we aimed to describe a case series and provide a systematic literature review on perampanel effects on PMEs.Methods: We report the perampanel effectiveness on myoclonus, daily life activities, and seizures on an original Italian multicenter case series of 11 individuals with PMEs. Then, using the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, we performed a systematic review on perampanel effect on myoclonus and disability in PMEs. We searched PubMed, Scopus, and Google Scholar articles on perampanel and PMEs up to June 2020. No prospective trials were found. We reviewed 11 case series manuscripts reporting 104 cases of different PMEs.Results: Here, we are reporting the effectiveness of perampanel in five individuals affected by Unverricht–Lundborg disease, three by Lafora disease, two by sialidosis, and one by an undetermined PME. Nine out of 11 individuals improved their disability related to the action myoclonus (two with Lafora disease did not). Among the 104 persons with PMEs collected by the systematic review, we found that more than half of the patients receiving perampanel exhibited an amelioration of action myoclonus and, consequently, of their independence in daily life activities. The Unverricht–Lundborg disease seemed to show the best clinical response to perampanel, in comparison with the other more severe PMEs. A significant seizure reduction was achieved by almost all persons with active epilepsy. Only 11% of PME patients dropped out due to inefficacy.Conclusions: Perampanel demonstrated a beneficial effect with regard to action myoclonus, disability, and seizures and was well-tolerated in people with PMEs, independently from their genetic diagnosis. Given the limited scientific evidence, broader prospective trials should be encouraged.

Mobile phone apps’ support for people with mild dementia: A systematic review (Preprint)

2019 ◽  
Author(s):  
Leona Cilar ◽  
Lucija Gosak ◽  
Amanda Briggs ◽  
Klavdija Čuček Trifkovič ◽  
Tracy McClelland ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Thematic Analysis ◽  
Daily Life ◽  
General Term ◽  
Cognitive Domain ◽  
Mild Dementia ◽  
Daily Life Activities ◽  
Human Needs ◽  
People With Dementia ◽  
System Usability Scale

BACKGROUND Dementia is a general term for various disorders characterized by memory impairment and loss of at least one cognitive domain. People with dementia are faced with different difficulties in their daily life activities (DLA). With the use of modern technologies, such as mobile phone apps – often called health apps, their difficulties can be alleviated. OBJECTIVE The aim of this paper was to systematically search, analyze and synthetize mobile phone apps designed to support people with mild dementia in daily life activities in two apps bases: Apple App Store and Google Play Store. METHODS A search was conducted in May 2019 following PRISMA recommendations. Results were analyzed and displayed as tables and graphs. Results were synthetized using thematic analysis which was conducted from 14 components, based on human needs for categorized nursing activities. Mobile phone apps were assessed for quality using the System Usability Scale. RESULTS A total of 15 mobile phone apps were identified applying inclusion and exclusion criteria. Five major themes were identified with thematic analysis: multi-component DLA, communication and feelings, recreation, eating and drinking, and movement. Most of the apps (73%) of the apps were not mentioned in scientific literature. CONCLUSIONS There are many mobile phone apps available in mobile phone markets for the support for people with mild dementia; yet only a few of them are focused on challenges in daily life activities. Most of the available apps were not evaluated nor assessed for quality.

scholarly journals AnkFall—Falls, Falling Risks and Daily-Life Activities Dataset with an Ankle-Placed Accelerometer and Training Using Recurrent Neural Networks

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1889
Author(s):  
Francisco Luna-Perejón ◽  
Luis Muñoz-Saavedra ◽  
Javier Civit-Masot ◽  
Anton Civit ◽  
Manuel Domínguez-Morales
Keyword(s):  
Elderly Person ◽  
Fall Detection ◽  
The Elderly ◽  
Daily Life Activities ◽  
Learning Classifier ◽  
The People ◽  
Activity Types ◽  
Remote Monitoring Systems ◽  
Secondary Study

Falls are one of the leading causes of permanent injury and/or disability among the elderly. When these people live alone, it is convenient that a caregiver or family member visits them periodically. However, these visits do not prevent falls when the elderly person is alone. Furthermore, in exceptional circumstances, such as a pandemic, we must avoid unnecessary mobility. This is why remote monitoring systems are currently on the rise, and several commercial solutions can be found. However, current solutions use devices attached to the waist or wrist, causing discomfort in the people who wear them. The users also tend to forget to wear the devices carried in these positions. Therefore, in order to prevent these problems, the main objective of this work is designing and recollecting a new dataset about falls, falling risks and activities of daily living using an ankle-placed device obtaining a good balance between the different activity types. This dataset will be a useful tool for researchers who want to integrate the fall detector in the footwear. Thus, in this work we design the fall-detection device, study the suitable activities to be collected, collect the dataset from 21 users performing the studied activities and evaluate the quality of the collected dataset. As an additional and secondary study, we implement a simple Deep Learning classifier based on this data to prove the system’s feasibility.

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