Evaluation of Feature Engineering on Wearable Sensor-based Fall Detection

Ramachandran, Anita; Ramesh, Adarsh; Karuppiah, Anupama

doi:10.1109/icoin48656.2020.9016479

Cited by 10 publications

(9 citation statements)

References 19 publications

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“…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%

“…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. We wish to stress here that no motivation to the choice of adding the RF classifier is provided and statistical tests for differences are not presented.…”

Section: Fall Detectionmentioning

confidence: 99%

“…We wish to stress here that no motivation to the choice of adding the RF classifier is provided and statistical tests for differences are not presented. 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: 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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Section: Fall Detectionmentioning

confidence: 99%

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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“…A CAS generally integrates a set of vision systems with cameras and other sensors, such as microphones or vibration sensors, placed in a welllit environment to monitor the user within the range of view [9]. Wearable sensors are commonly used to analyze a human's fall activities, based on the motion pattern [10] and physiological status [11]. Even though FDSs have been widely deployed in the healthcare sector, especially for elderly people, the system is targeted at detecting slowfalling movements, which is not suitable for firefighters.…”

Section: Introductionmentioning

confidence: 99%

Smart Wearables with Sensor Fusion for Fall Detection in Firefighting

Chai

Pike

et al. 2021

Sensors

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During the past decade, falling has been one of the top three causes of death amongst firefighters in China. Even though there are many studies on fall-detection systems (FDSs), the majority use a single motion sensor. Furthermore, few existing studies have considered the impact sensor placement and positioning have on fall-detection performance; most are targeted toward fall detection of the elderly. Unfortunately, floor cracks and unstable building structures in the fireground increase the difficulty of detecting the fall of a firefighter. In particular, the movement activities of firefighters are more varied; hence, distinguishing fall-like activities from actual falls is a significant challenge. This study proposed a smart wearable FDS for firefighter fall detection by integrating motion sensors into the firefighter’s personal protective clothing on the chest, elbows, wrists, thighs, and ankles. The firefighter’s fall activities are detected by the proposed multisensory recurrent neural network, and the performances of different combinations of inertial measurement units (IMUs) on different body parts were also investigated. The results indicated that the sensor fusion of IMUs from all five proposed body parts achieved performances of 94.10%, 92.25%, and 94.59% in accuracy, sensitivity, and specificity, respectively.

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