Wearable-sensor-based pre-impact fall detection system with a hierarchical classifier

Yue, Wu; Su, Yi-Wen; Ran, Feng; Yu, Ning; Zang, Xu

doi:10.1016/j.measurement.2019.04.002

Cited by 47 publications

(37 citation statements)

References 21 publications

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“…Based on whether the user’s interference is necessary, current fall detection methods can be classified into two categories: user-dependent and user-independent. The user-dependent methods either use wearable devices (e.g., pendant, watch, clip, bracelet, or ring) to track rapid changes in its orientation or rely on users themselves to report the emergency of fall by interacting (e.g., pressing) the wearable device [4,5,6,7,8,9]. These options suffer from limitations such as getting the person to wear the device or false-positive detection (e.g., a fall is incorrectly identified when no fall occurs) due to user’s abrupt movement.…”

Section: Introductionmentioning

confidence: 99%

Detection of Human Fall Using Floor Vibration and Multi-Features Semi-Supervised SVM

Liu

Jiang

et al. 2019

Sensors

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Human falls are the premier cause of fatal and nonfatal injuries among older adults. The health outcome of a fall event is largely dependent on rapid response and rescue of the fallen elder. Being able to provide an accurate and fast fall detection will dramatically improve the health outcomes of the older population and reduce the associated healthcare cost after a fall. To achieve the goal, a multi-features semi-supervised support vector machines (MFSS-SVM) algorithm utilizing measurements from structural floor vibration obtained through accelerometers is proposed in this study to detect falling events with limited labeled samples. In this MFSS-SVM algorithm, the peak value, energy, and correlation coefficient of the accelerometer signal are used as classification features. The performance of the proposed algorithm was validated with laboratory experiments among activities including falling, walking, free jumping, rhythmic jumping, bag dropping, and ball dropping. To further illustrate the performance of the algorithm, a benchmark database was adopted and expanded to test its ability to accurately identify falling, compared with the algorithm used in the benchmark study. Results show that by using the proposed algorithm, the falling events can be identified with high accuracy and confidence, even with small training datasets and test nodes.

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Section: Introductionmentioning

confidence: 99%

Detection of Human Fall Using Floor Vibration and Multi-Features Semi-Supervised SVM

Liu

Jiang

et al. 2019

Sensors

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“…The multiphase fall model divides a fall process into several fall phases. Various types of fall models have been proposed for the analysis of fall events [ 17 , 23 , 24 , 25 , 26 ]. There are two common types of fall models.…”

Section: Methodsmentioning

confidence: 99%

Multiphase Identification Algorithm for Fall Recording Systems Using a Single Wearable Inertial Sensor

Hsieh

Huang²,

Liu

et al. 2021

Sensors

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Fall-related information can help clinical professionals make diagnoses and plan fall prevention strategies. The information includes various characteristics of different fall phases, such as falling time and landing responses. To provide the information of different phases, this pilot study proposes an automatic multiphase identification algorithm for phase-aware fall recording systems. Seven young adults are recruited to perform the fall experiment. One inertial sensor is worn on the waist to collect the data of body movement, and a total of 525 trials are collected. The proposed multiphase identification algorithm combines machine learning techniques and fragment modification algorithm to identify pre-fall, free-fall, impact, resting and recovery phases in a fall process. Five machine learning techniques, including support vector machine, k-nearest neighbor (kNN), naïve Bayesian, decision tree and adaptive boosting, are applied to identify five phases. Fragment modification algorithm uses the rules to detect the fragment whose results are different from the neighbors. The proposed multiphase identification algorithm using the kNN technique achieves the best performance in 82.17% sensitivity, 85.74% precision, 73.51% Jaccard coefficient, and 90.28% accuracy. The results show that the proposed algorithm has the potential to provide automatic fine-grained fall information for clinical measurement and assessment.

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“…They employed the SVM, KNN, and random forest (RF) for fall event identification and the fall associated activity recognition using a series of experiments data. Another solution is described in [26] concerning the prior detection of fall using angles and angular‐velocity measurement gathered by two wearable sensors placed in the human waist and thigh. In this work, the performances of the hierarchical Fisher's discriminant analysis classifier are investigated when identifying non‐fall, backward fall, and forward fall to avoid false alarms.…”

Section: Introductionmentioning

confidence: 99%

Machine learning‐based edge‐computing on a multi‐level architecture of WSN and IoT for real‐time fall detection

Attaoui

Largo²,

Kaissari

et al. 2020

IET wirel. sens. syst.

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Wearable-sensor-based pre-impact fall detection system with a hierarchical classifier

Cited by 47 publications

References 21 publications

Detection of Human Fall Using Floor Vibration and Multi-Features Semi-Supervised SVM

Detection of Human Fall Using Floor Vibration and Multi-Features Semi-Supervised SVM

Multiphase Identification Algorithm for Fall Recording Systems Using a Single Wearable Inertial Sensor

Machine learning‐based edge‐computing on a multi‐level architecture of WSN and IoT for real‐time fall detection

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