Detection of Pre-Impact Falls from Heights Using an Inertial Measurement Unit Sensor

Kim, Young‐Ho; Jung, Haneul; Koo, Bummo; Kim, Jong Man; Kim, Tae-Hee; Nam, Yejin

doi:10.3390/s20185388

Cited by 15 publications

(8 citation statements)

References 33 publications

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“…For safety reasons, a dummy (Madamade, Chuncheon, Gangwon-do, Korea) (height: 180 cm, weight: 10 kg) was used for the HF experiments with fall heights higher than 2 m. Vertical and forward falling movements (FFH) were performed by the dummy at heights of 2 m and 3 m. All movements were repeated five times. Our previous study [15] revealed no differences between the data obtained when a person fell forward from 0.7 m and when a dummy fell forward from the same height using an SPSS-based independent sample t-test.…”

Section: Methodsmentioning

confidence: 84%

“…Several studies have been conducted on developing FFH detection algorithms by extending the aforementioned research [2,14,15]. Yang, et al [2] performed near-miss fall detection based on machine learning with IMU sensor data, where the algorithm showed 86.8% accuracy in the laboratory and 85.2% accuracy outdoors.…”

Section: Introductionmentioning

confidence: 99%

“…Dogan and Akcamete [14] performed an FFH detection study by calculating the fall height from three-axis acceleration data, with an overall error rate of 10.8%. Kim et al [15] developed an FFH detection algorithm by calculating the vertical velocity and the trunk angles from IMU data and reported that 100% accuracy and a lead time of 301.8 ± 87.8 ms were obtained.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fall-from-Height Detection Using Deep Learning Based on IMU Sensor Data for Accident Prevention at Construction Sites

Lee

Koo

Yang

et al. 2022

Sensors

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Workers at construction sites are prone to fall-from-height (FFH) accidents. The severity of injury can be represented by the acceleration peak value. In the study, a risk prediction against FFH was made using IMU sensor data for accident prevention at construction sites. Fifteen general working movements (NF: non-fall), five low-hazard-fall movements, (LF), and five high-hazard-FFH movements (HF) were performed by twenty male subjects and a dummy. An IMU sensor was attached to the T7 position of the subject to measure the three-axis acceleration and angular velocity. The peak acceleration value, calculated from the IMU data, was 4 g or less in general work movements and 9 g or more in FFHs. Regression analysis was performed by applying various deep learning models, including 1D-CNN, 2D-CNN, LSTM, and Conv-LSTM, to the risk prediction, and then comparing them in terms of their mean absolute error (MAE) and mean squared error (MSE). The FFH risk level was estimated based on the predicted peak acceleration. The Conv-LSTM model trained by MAE showed the smallest error (MAE: 1.36 g), and the classification with the predicted peak acceleration showed the best accuracy (97.6%). This study successfully predicted the FFH risk levels and could be helpful to reduce fatal injuries at construction sites.

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Section: Methodsmentioning

confidence: 84%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fall-from-Height Detection Using Deep Learning Based on IMU Sensor Data for Accident Prevention at Construction Sites

Lee

Koo

Yang

et al. 2022

Sensors

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“…In addition, three different types of algorithms for pre-impact fall detection were implemented based on this comprehensive motion dataset. All of them were adopted from the state-ofthe-art algorithms published recently (Jung et al, 2020;Kim et al, 2020;Yu et al, 2020Yu et al, , 2021 and thus were representative to be the benchmarks. It was expected that the thresholdbased algorithm showed poorer performance compared with machine learning algorithms (SVM and ConvLSTM) since the number of motion features considered for the threshold-based algorithm was much less than the other two algorithms.…”

Section: Discussionmentioning

confidence: 99%

“…For the threshold-based algorithm, four thresholds (magnitude of acceleration, pitch angle, roll angle, and vertical velocity) were considered to detect a pre-impact fall based on recent publications (Jung et al, 2020;Kim et al, 2020). The magnitude of the acceleration is the L-2 norm of acceleration readings from three axes.…”

Section: Benchmark Algorithms For Pre-impact Fall Detectionmentioning

confidence: 99%

A Large-Scale Open Motion Dataset (KFall) and Benchmark Algorithms for Detecting Pre-impact Fall of the Elderly Using Wearable Inertial Sensors

Jang

Xiong

2021

Front. Aging Neurosci.

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Research on pre-impact fall detection with wearable inertial sensors (detecting fall accidents prior to body-ground impacts) has grown rapidly in the past decade due to its great potential for developing an on-demand fall-related injury prevention system. However, most researchers use their own datasets to develop fall detection algorithms and rarely make these datasets publicly available, which poses a challenge to fairly evaluate the performance of different algorithms on a common basis. Even though some open datasets have been established recently, most of them are impractical for pre-impact fall detection due to the lack of temporal labels for fall time and limited types of motions. In order to overcome these limitations, in this study, we proposed and publicly provided a large-scale motion dataset called “KFall,” which was developed from 32 Korean participants while wearing an inertial sensor on the low back and performing 21 types of activities of daily living and 15 types of simulated falls. In addition, ready-to-use temporal labels of the fall time based on synchronized motion videos were published along with the dataset. Those enhancements make KFall the first public dataset suitable for pre-impact fall detection, not just for post-fall detection. Importantly, we have also developed three different types of latest algorithms (threshold based, support-vector machine, and deep learning), using the KFall dataset for pre-impact fall detection so that researchers and practitioners can flexibly choose the corresponding algorithm. Deep learning algorithm achieved both high overall accuracy and balanced sensitivity (99.32%) and specificity (99.01%) for pre-impact fall detection. Support vector machine also demonstrated a good performance with a sensitivity of 99.77% and specificity of 94.87%. However, the threshold-based algorithm showed relatively poor results, especially the specificity (83.43%) was much lower than the sensitivity (95.50%). The performance of these algorithms could be regarded as a benchmark for further development of better algorithms with this new dataset. This large-scale motion dataset and benchmark algorithms could provide researchers and practitioners with valuable data and references to develop new technologies and strategies for pre-impact fall detection and proactive injury prevention for the elderly.

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Tag and IoT based safety hook monitoring for prevention of falls from height

Khan

Khalid

Anjum

et al. 2022

Automation in Construction

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Detection of Pre-Impact Falls from Heights Using an Inertial Measurement Unit Sensor

Cited by 15 publications

References 33 publications

Fall-from-Height Detection Using Deep Learning Based on IMU Sensor Data for Accident Prevention at Construction Sites

Fall-from-Height Detection Using Deep Learning Based on IMU Sensor Data for Accident Prevention at Construction Sites

A Large-Scale Open Motion Dataset (KFall) and Benchmark Algorithms for Detecting Pre-impact Fall of the Elderly Using Wearable Inertial Sensors

Tag and IoT based safety hook monitoring for prevention of falls from height

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