Performance Analysis of Machine Learning Algorithms for Fall Detection

Ramachandran, Anita; Ramesh, Adarsh; Pahwa, Piyush; Atreyaa, A. Prahalad; Murari, Shivam; Anupama, K. R.

doi:10.1109/healthcom46333.2019.9009442

Cited by 3 publications

(5 citation statements)

References 20 publications

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“…Motivation for replacing SVM with XGBoost in [52] is also lacking. We find it peculiar that SVM was replaced since SVM outperformed NB in the other works [13,51].…”

Section: Fall Detectionmentioning

confidence: 92%

“…Again, kNN performed best. In a follow up work, Ramachandran et al (2019Ramachandran et al ( , 2020 [51,52] explored the effect on performance of combining IMU and HR data in fall detection. A labelled dataset was created by asking 10 young healthy participants (gender not reported) to perform 14 ADLs and six different falls, twice, while wearing a smartwatch.…”

Section: Fall Detectionmentioning

confidence: 99%

“…A labelled dataset was created by asking 10 young healthy participants (gender not reported) to perform 14 ADLs and six different falls, twice, while wearing a smartwatch. RF, which was added as an additional classifier in [51], performed best and the performance increased when adding HR as a feature. Using the same dataset [52] replaced SVM with XGBoost, RF outperformed also XGBoost.…”

Section: Fall Detectionmentioning

confidence: 99%

“…Several of these risk factors have also been identified in this section, ten works about which we have reported on, retrieved during the literature search, and on another three works [27,51,52]. Despite the fact that both age and gender affect fall risk, and potentially, also, injuries after a fall, only a total of 14 older adults (who were aged 61-66) were recruited to the studies.…”

Section: Fall Detectionmentioning

confidence: 99%

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A Systematic Review of Wearable Sensors for Monitoring Physical Activity

Kristoffersson

Lindén

2022

Sensors

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This article reviews the use of wearable sensors for the monitoring of physical activity (PA) for different purposes, including assessment of gait and balance, prevention and/or detection of falls, recognition of various PAs, conduction and assessment of rehabilitation exercises and monitoring of neurological disease progression. The article provides in-depth information on the retrieved articles and discusses study shortcomings related to demographic factors, i.e., age, gender, healthy participants vs patients, and study conditions. It is well known that motion patterns change with age and the onset of illnesses, and that the risk of falling increases with age. Yet, studies including older persons are rare. Gender distribution was not even provided in several studies, and others included only, or a majority of, men. Another shortcoming is that none of the studies were conducted in real-life conditions. Hence, there is still important work to be done in order to increase the usefulness of wearable sensors in these areas. The article highlights flaws in how studies based on previously collected datasets report on study samples and the data collected, which makes the validity and generalizability of those studies low. Exceptions exist, such as the promising recently reported open dataset FallAllD, wherein a longitudinal study with older adults is ongoing.

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“…Motivation for replacing SVM with XGBoost in [52] is also lacking. We find it peculiar that SVM was replaced since SVM outperformed NB in the other works [13,51].…”

Section: Fall Detectionmentioning

confidence: 92%

Section: Fall Detectionmentioning

confidence: 99%

Section: Fall Detectionmentioning

confidence: 99%

Section: Fall Detectionmentioning

confidence: 99%

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A Systematic Review of Wearable Sensors for Monitoring Physical Activity

Kristoffersson

Lindén

2022

Sensors

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“…Experiments were performed across 20 different ADL/fall activity simulations, such as walking, running, climbing stairs, abrupt movements and various types of falls, in controlled environments. One of our previous papers describes the details on the fall simulation and dataset collection process [26]. Basic preprocessing was then performed on the data set.…”

Section: Data Collection Pre-processing and Baseline Performance Prof...mentioning

confidence: 99%

Machine learning algorithms for fall detection using kinematic and heart rate parameters-a comprehensive analysis

Ramachandran

Ramesh

Sukhlecha

et al. 2020

IJ-AI

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The application of machine learning techniques to detect and classify falls is a prominent area of research in the domain of intelligent assisted living systems. Machine learning (ML) based solutions for fall detection systems built on wearable devices use various sources of information such inertial motion units (IMU), vital signs, acoustic or channel state information parameters. Most existing research rely on only one of these sources; however, a need to do more experimenation to observe the efficiency of the ML classifiers while coupling features from diverse sources, was felt. In addition, fall detection systems based on wearable devices, require intelligent feature engineering and selection for dimensionality reduction, so as to reduce the computational complexity of the devices. In this paper we do a comprehensive performance analysis of ML classifiers for fall detection, on a dataset we collected. The analysis includes the impact of the following aspects on the performance of ML classifiers for fall detection: (i) using a combination of features from 2 sensors-an IMU sensor and a heart rate sensor, (ii) feature engineering and feature selection based on statistical methods, and (iii) using ensemble techniques for fall detection. We find that the inclusion of heart rate along with IMU sensor parameters improves the accuracy of fall detection. The conclusions from our experimentations on feature selection and ensemble analysis can serve as inputs for researchers designing wearable device-based fall detection systems.

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