A direction-sensitive fall detection system using single 3D accelerometer and learning classifier

Hossain, Md. Farhad; Ali, Md. Liakot; Islam, Md. Zahurul; Mustafa, Hossen Asiful

doi:10.1109/meditec.2016.7835372

Cited by 26 publications

(15 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3) Classification: SVM has been widely applied in fall detection studies [16], [21], [27], [38]- [41]. We used SVM to classify the PCA values ( P k×l ) obtained in Subsection III-B-2 into cycling status ( y i = 1) and falling status ( y i = −1).…”

Section: B Design Of Softwarementioning

confidence: 99%

“…The proposed system applies principal component analysis (PCA), which has been widely used in engineering fields to reduce the dimensionality [16], [20]- [22], [36], [37], to these 24 MARG features to reduce their dimensionality. To classify bicycle accident events, the proposed system adopts support vector machines (SVM), which has been widely used for classification in science and engineering studies [16], [21], [27], [38]- [41]. The rest of this article is organized as follows: Section II describes the materials for our proposed accident detection system.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Accident Detection System for Bicycle Riders

2021

View full text Add to dashboard Cite

Bicycle riders are exposed to accident injuries such as head trauma. The risk of these riders' injuries is higher compared to the risk of injuries for motorists. Crashes, riders' errors, and environmental hazards are the cause of bicycle-related accidents. In 2017, nearly 50% of bicycle-related accidents occurred in urban areas at night, which may contribute to a delay in reporting the accidents to emergency centers. Hence, a system that can detect the accident is needed to notify urgent care clinics promptly. In this article, we propose a bicycle accident detection system. We designed hardware modules measuring the features related to the riding status of a bicycle and fall accidents. For this purpose, we used a magnetic, angular rate, and gravity (MARG) sensor-based system which measures four different types of signals: 1) acceleration, 2) angular velocity, 3) angle, and 4) magnitude of the riding status. Each of these signals is measured in three different directions (X, Y, and Z). We used two different time-domain parameters, i.e., average and standard deviation. As a result, we considered 24 features. We used principal component analysis (PCA) for feature reduction and the support vector machines (SVM) algorithm for the detection of fall accidents. Experimental results show that our proposed system detects fall accidents during cycling status with 95.2% accuracy, which demonstrates the feasibility of our proposed bicycle accident detection system. Index Terms-Bicycle accident, fall detection, MARG, principal component analysis, support vector machines. I. INTRODUCTION A CCORDING to the Centers for Disease Control and Prevention's (CDC) report published in 2017 [1], bicycle-related accidents lead to around 1,000 fatal and 467,000 non-fatal injuries. There are various causes associated with these bicycle-related accidents such as rider's carelessness, environmental hazards, and crashes with motor vehicles or other bicycles [2]. More than 45% of bicycle-related accidents are reported to occur in dark conditions and urban areas [1], [3]. Moreover, in some situations, the rider may be alone or unable to ask for help due to shock, injuries, or unconsciousness, which can delay medical treatment [4]. There have been studies on analyzing and monitoring riding conditions to increase the safety of bicycle riders [3], [5]-[10]. In [9], a smartphone is used to recognize four different types of riding status-right turn, left turn, straight run, and stop. Here, the smartphone was attached to a bicycle handlebar and collected accelerometer and gyroscope data to recognize the movement type of the bicycle based on Manuscript

show abstract

Section: B Design Of Softwarementioning

confidence: 99%

mentioning

confidence: 99%

Accident Detection System for Bicycle Riders

2021

View full text Add to dashboard Cite

show abstract

“…Furthermore, a comprehensive study from Bagala et al [ 16 ] shows that the existing threshold-based approaches produce a high number of false alarms. One possible reason behind that high number of false alarms is that manually defined thresholds do not generalize well for unseen subjects [ 17 ]. Several studies used machine learning to construct a classifier that distinguishes falls from ADLs, so that the number of both false alarms and undetected falls can be reduced.…”

Section: Introductionmentioning

confidence: 99%

An Event-Triggered Machine Learning Approach for Accelerometer-Based Fall Detection

Putra

Brusey

Gaura

et al. 2017

Sensors

View full text Add to dashboard Cite

The fixed-size non-overlapping sliding window (FNSW) and fixed-size overlapping sliding window (FOSW) approaches are the most commonly used data-segmentation techniques in machine learning-based fall detection using accelerometer sensors. However, these techniques do not segment by fall stages (pre-impact, impact, and post-impact) and thus useful information is lost, which may reduce the detection rate of the classifier. Aligning the segment with the fall stage is difficult, as the segment size varies. We propose an event-triggered machine learning (EvenT-ML) approach that aligns each fall stage so that the characteristic features of the fall stages are more easily recognized. To evaluate our approach, two publicly accessible datasets were used. Classification and regression tree (CART), k-nearest neighbor (k-NN), logistic regression (LR), and the support vector machine (SVM) were used to train the classifiers. EvenT-ML gives classifier F-scores of 98% for a chest-worn sensor and 92% for a waist-worn sensor, and significantly reduces the computational cost compared with the FNSW- and FOSW-based approaches, with reductions of up to 8-fold and 78-fold, respectively. EvenT-ML achieves a significantly better F-score than existing fall detection approaches. These results indicate that aligning feature segments with fall stages significantly increases the detection rate and reduces the computational cost.

show abstract

“…Jefiza et al [32] use backpropagation neural network (BPNN) for fall detection, with data collected from 3-axis accelerometer and gyroscope, and reported an accuracy of 98.182%, precision of 98.33%, sensitivity of 95.161%, and specificity of 99.367%. Hossain et al [33] also attempt to distinguish falls from ADLs and compares SVM, kNN, and complex tree algorithms applied on data generated by accelerometers. e paper compared the performance of these algorithms with respect to accuracy, precision, and recall, on ADLs and four types of falls (forward, backward, right, and left).…”

Section: Machine Learning-based Wearable Systems For Fallmentioning

confidence: 99%

A Survey on Recent Advances in Wearable Fall Detection Systems

Ramachandran

Karuppiah

2020

BioMed Research International

108

View full text Add to dashboard Cite

With advances in medicine and healthcare systems, the average life expectancy of human beings has increased to more than 80 yrs. As a result, the demographic old-age dependency ratio (people aged 65 or above relative to those aged 15–64) is expected to increase, by 2060, from ∼28% to ∼50% in the European Union and from ∼33% to ∼45% in Asia (Ageing Report European Economy, 2015). Therefore, the percentage of people who need additional care is also expected to increase. For instance, per studies conducted by the National Program for Health Care of the Elderly (NPHCE), elderly population in India will increase to 12% of the national population by 2025 with 8%–10% requiring utmost care. Geriatric healthcare has gained a lot of prominence in recent years, with specific focus on fall detection systems (FDSs) because of their impact on public lives. According to a World Health Organization report, the frequency of falls increases with increase in age and frailty. Older people living in nursing homes fall more often than those living in the community and 40% of them experience recurrent falls (World Health Organization, 2007). Machine learning (ML) has found its application in geriatric healthcare systems, especially in FDSs. In this paper, we examine the requirements of a typical FDS. Then we present a survey of the recent work in the area of fall detection systems, with focus on the application of machine learning. We also analyze the challenges in FDS systems based on the literature survey.

show abstract

A direction-sensitive fall detection system using single 3D accelerometer and learning classifier

Cited by 26 publications

References 15 publications

Accident Detection System for Bicycle Riders

Accident Detection System for Bicycle Riders

An Event-Triggered Machine Learning Approach for Accelerometer-Based Fall Detection

A Survey on Recent Advances in Wearable Fall Detection Systems

Contact Info

Product

Resources

About