A Comparison of Machine Learning Algorithms for Fall Detection using Wearable Sensors

Zurbuchen, Nicolas; Bruegger, Pascal; Wilde, Adriana

doi:10.1109/icaiic48513.2020.9065205

Cited by 24 publications

(18 citation statements)

References 21 publications

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“…These are: the period before the fall happens (pre-fall), the fall itself (impact) and after the fall happened (post-fall). This research question extends our previous work [8].…”

Section: Introductionsupporting

confidence: 86%

“…We then experimented on the filtered data with five ML algorithms. This research question extends our previous work [ 8 ]. What is the difference in performance across various types of ML algorithms by adopting a multi-class approach for identifying phases of a fall?…”

Section: Introductionsupporting

confidence: 84%

“…We then experimented on the filtered data with five ML algorithms. This research question extends our previous work [ 8 ].…”

Section: Introductionsupporting

confidence: 84%

“…This paper is an extension of our work accepted at the ICAIIC 2020 [ 8 ]. This paper has three research questions: What is the difference in performance across various types of Machine Learning (ML) algorithms in a FDS?…”

Section: Introductionmentioning

confidence: 99%

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A Machine Learning Multi-Class Approach for Fall Detection Systems Based on Wearable Sensors with a Study on Sampling Rates Selection

Zurbuchen

Wilde

Bruegger

2021

Sensors

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Falls are dangerous for the elderly, often causing serious injuries especially when the fallen person stays on the ground for a long time without assistance. This paper extends our previous work on the development of a Fall Detection System (FDS) using an inertial measurement unit worn at the waist. Data come from SisFall, a publicly available dataset containing records of Activities of Daily Living and falls. We first applied a preprocessing and a feature extraction stage before using five Machine Learning algorithms, allowing us to compare them. Ensemble learning algorithms such as Random Forest and Gradient Boosting have the best performance, with a Sensitivity and Specificity both close to 99%. Our contribution is: a multi-class classification approach for fall detection combined with a study of the effect of the sensors’ sampling rate on the performance of the FDS. Our multi-class classification approach splits the fall into three phases: pre-fall, impact, post-fall. The extension to a multi-class problem is not trivial and we present a well-performing solution. We experimented sampling rates between 1 and 200 Hz. The results show that, while high sampling rates tend to improve performance, a sampling rate of 50 Hz is generally sufficient for an accurate detection.

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“…These are: the period before the fall happens (pre-fall), the fall itself (impact) and after the fall happened (post-fall). This research question extends our previous work [8].…”

Section: Introductionsupporting

confidence: 86%

Section: Introductionsupporting

confidence: 84%

“…We then experimented on the filtered data with five ML algorithms. This research question extends our previous work [ 8 ].…”

Section: Introductionsupporting

confidence: 84%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Machine Learning Multi-Class Approach for Fall Detection Systems Based on Wearable Sensors with a Study on Sampling Rates Selection

Zurbuchen

Wilde

Bruegger

2021

Sensors

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“…In the context of the SEMS project, we integrated the result of a master thesis on fall detection for elderly [8]. Falls are one of the leading causes of death among the elderly [9].…”

Section: Fall Detection For Elderly Peoplementioning

confidence: 99%

On the Development of a Resident Monitoring System: Usability, Privacy and Security aspects

Bruegger

Wilde

Guibert

2020

2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE

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Worldwide, the elderly have suffered disproportionately from the effects of the COVID-19 pandemic, both in terms of their prognosis once contracted the disease and in terms of the preventative measures required for this demographic, who are at much higher risk than the rest of the population. In the "new normal", the well-being of older adults (residing either in their own homes or in care homes) will be ideally monitored remotely. These measures would preserve the independence of individuals without compromising on their safety. In this paper we discuss aspects of the design and implementation of a resident monitoring system (RMS) with particular emphasis on overcoming the barriers for adoption among these populations, by addressing the aspects of usability, privacy and security at the core of the development of such a system. We discuss the current challenges of this research and future work on the RMS.

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Mobile Health Solution Through Machine Learning and Sensors in the Detection of Falls Associated with Aging

Simões,

de Oliveira,

da Silva

et al. 2023

Current Trends in Biomedical Engineering

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A Comparison of Machine Learning Algorithms for Fall Detection using Wearable Sensors

Cited by 24 publications

References 21 publications

A Machine Learning Multi-Class Approach for Fall Detection Systems Based on Wearable Sensors with a Study on Sampling Rates Selection

A Machine Learning Multi-Class Approach for Fall Detection Systems Based on Wearable Sensors with a Study on Sampling Rates Selection

On the Development of a Resident Monitoring System: Usability, Privacy and Security aspects

Mobile Health Solution Through Machine Learning and Sensors in the Detection of Falls Associated with Aging

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