Latest Research Trends in Fall Detection and Prevention Using Machine Learning: A Systematic Review

Falls are unusual actions that cause a significant health risk among older people. The growing percentage of people of old age requires urgent development of fall detection and prevention systems. The emerging technology focuses on developing such systems to improve quality of life, especially for the elderly. A fall prevention system tries to predict and reduce the risk of falls. In contrast, a fall detection system observes the fall and generates a help notification to minimize the consequences of falls. A plethora of technical and review papers exist in the literature with a primary focus on fall detection. Similarly, several studies are relatively old, with a focus on wearables only, and use statistical and threshold-based approaches with a high false alarm rate. Therefore, this paper presents the latest research trends in fall detection and prevention systems using Machine Learning (ML) algorithms. It uses recent studies and analyzes datasets, age groups, ML algorithms, sensors, and location. Additionally, it provides a detailed discussion of the current trends of fall detection and prevention systems with possible future directions. This overview can help researchers understand the current systems and propose new methodologies by improving the highlighted issues.

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Machine Learning based Human Fall Detection System

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.35394 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 2677-2682

Author(s):

Nishanth P

Keyword(s):

Machine Learning ◽

Detection System ◽

Wearable Sensors ◽

Fall Detection ◽

The Elderly ◽

World Health ◽

Support Vector ◽

Data Output ◽

Health Organization ◽

Context Features

Falls have become one of the reasons for death. It is common among the elderly. According to World Health Organization (WHO), 3 out of 10 living alone elderly people of age 65 and more tend to fall. This rate may get higher in the upcoming years. In recent years, the safety of elderly residents alone has received increased attention in a number of countries. The fall detection system based on the wearable sensors has made its debut in response to the early indicator of detecting the fall and the usage of the IoT technology, but it has some drawbacks, including high infiltration, low accuracy, poor reliability. This work describes a fall detection that does not reliant on wearable sensors and is related on machine learning and image analysing in Python. The camera's high-frequency pictures are sent to the network, which uses the Convolutional Neural Network technique to identify the main points of the human. The Support Vector Machine technique uses the data output from the feature extraction to classify the fall. Relatives will be notified via mobile message. Rather than modelling individual activities, we use both motion and context information to recognize activities in a scene. This is based on the notion that actions that are spatially and temporally connected rarely occur alone and might serve as background for one another. We propose a hierarchical representation of action segments and activities using a two-layer random field model. The model allows for the simultaneous integration of motion and a variety of context features at multiple levels, as well as the automatic learning of statistics that represent the patterns of the features.

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Machine learning-based fall detection system for the elderly using passive RFID sensor tags

2019 13th International Conference on Sensing Technology (ICST) ◽

10.1109/icst46873.2019.9047732 ◽

2019 ◽

Author(s):

Koichi Toda ◽

Norihiko Shinomiya

Keyword(s):

Machine Learning ◽

Detection System ◽

Fall Detection ◽

The Elderly ◽

Passive Rfid

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Wearable Based-Sensor Fall Detection System Using Machine Learning Algorithm

2021 8th International Conference on Computer and Communication Engineering (ICCCE) ◽

10.1109/iccce50029.2021.9467195 ◽

2021 ◽

Author(s):

Anis Nadia Ishak ◽

Mohamed Hadi Habaebi ◽

Siti Hajar Yusoff ◽

Md. Rafiqul Islam

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Detection System ◽

Fall Detection ◽

Machine Learning Algorithm

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HYPERTENSION IN ELDERLY:OUR CURRENT TRENDS

10.36106/ijsr/5704603 ◽

2021 ◽

pp. 56-57

Author(s):

Rohit Arora ◽

D.K Sharma

Keyword(s):

Blood Pressure ◽

Elderly Patients ◽

The Elderly ◽

Common Disease ◽

Old Patients ◽

Current Trends ◽

Increased Risk ◽

Individualized Approach ◽

Risk Of Falls ◽

Optimal Target

Hypertension is a common disease in the elderly associated with signicant morbidity and mortality. Due to the complexity of this population, the optimal target of blood pressure (BP) control is still controversial. In this article, we conduct a literature review of trials published in English in the last 10 years which were specically designed to study the efcacy and safety of various BP targets in patients who are 70 years or older. Using these criteria, we found that the benets in the positive studies were demonstrated even with a minimal BPcontrol (systolic BP[SBP] <150 mmHg) and continued to be reported for a SBP<120 mmHg. On the other hand, keeping SBP<140 mmHg seemed to be safely achieved in elderly patients. Although the safety of lowering SBP to <120 mmHg is debated, Systolic Blood Pressure Intervention Trial study has shown no increased risk of falls, fractures, or kidney failure in elderly patients with SBP lower than this threshold. While the recent guidelines recommended to keep BP <130/80 mmHg in the elderly, more individualized approach should be considered to achieve this goal in order to avoid undesirable complications. Furthermore, further studies are required to evaluate BPtarget in very old patients or those with multiple comorbidities.

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Preimpact Fall Detection for Elderly Based on Fractional Domain

Mathematical Problems in Engineering ◽

10.1155/2021/6661034 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Ning Liu ◽

Dedi Zhang ◽

Zhong Su ◽

Tianrun Wang

Keyword(s):

Social Cost ◽

Detection System ◽

Fall Detection ◽

The Elderly ◽

Selection Method ◽

Excellent Performance ◽

Motion Data ◽

Algorithm Framework ◽

Warning Effect ◽

Mean Filtering

The aging population has become a growing worldwide problem. Every year, deaths and injuries caused by elderly people's falls bring huge social costs. To reduce the rate of injury and death caused by falls among the elderly and the following social cost, the elderly must be monitored. In this context, falls detecting has become a hotspot for many research institutions and enterprises at home and abroad. This paper proposes an algorithm framework to prealarm the fall based on fractional domain, using inertial data sensor as motion data collection devices, preprocessing the data by axis synthesis and mean filtering, and using fractional-order Fourier transform to convert the collected data from time domain to fractional domain. Based on the above, a multilayer dichotomy classifier is designed, and each node parameter selection method is given, which constructed a preimpact fall detection system with excellent performance. The experiment result demonstrates that the algorithm proposed in this paper can guarantee better warning effect and classification accuracy with fewer features.

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