HMM-Based Human Fall Detection and Prediction Method Using Tri-Axial Accelerometer

Tong, Lina; Song, Quanjun; Ge, Yaojun; Liu, Ming

doi:10.1109/jsen.2013.2245231

Cited by 197 publications

(44 citation statements)

References 18 publications

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“…As observed in Figure 3, it is found that most fall detection algorithm accuracies are above 90%. Accuracy of the work by [11,31] even reach 100%, which means that all falls were accurately detected in their testing scenarios. The lowest one is in [4], which reaches 79.57%.…”

Section: Performances Of Fall Detection Systemsmentioning

confidence: 89%

New Advances and Challenges of Fall Detection Systems: A Survey

2018

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Falling, as one of the main harm threats to the elderly, has drawn researchers' attentions and has always been one of the most valuable research topics in the daily health-care for the elderly in last two decades. Before 2014, several researchers reviewed the development of fall detection, presented issues and challenges, and navigated the direction for the study in the future. With smart sensors and Internet of Things (IoT) developing rapidly, this field has made great progress. However, there is a lack of a review and discussion on novel sensors, technologies and algorithms introduced and employed from 2014, as well as the emerging challenges and new issues. To bridge this gap, we present an overview of fall detection research and discuss the core research questions on this topic. A total of 6830 related documents were collected and analyzed based on the key words. Among these documents, the twenty most influential and highly cited articles are selected and discussed profoundly from three perspectives: sensors, algorithms and performance. The findings would assist researchers in understanding current developments and barriers in the systems of fall detection. Although researchers achieve fruitful work and progress, this research domain still confronts challenges on theories and practice. In the near future, the new solutions based on advanced IoT will sustainably urge the development to prevent falling injuries.

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Section: Performances Of Fall Detection Systemsmentioning

confidence: 89%

New Advances and Challenges of Fall Detection Systems: A Survey

2018

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“…In this subsection, we compare our method with the personalization algorithms proposed in the [29,31] as well as fall detection methods reported in [22,27]. The methods proposed in [29,31] have similar workflows for building a personalized classifier.…”

Section: Methodsmentioning

confidence: 99%

“…Tong et al [22] proposed a method based on the hidden Markov model (HMM), using tri-axial accelerations, to detect falls. They used the acceleration time series of fall processes before the collision to train the HMM model.…”

Section: Related Workmentioning

confidence: 99%

“…Shi et al [27] uses J48 decision tree, which is an efficient algorithm derived from C4.5 decision tree [28], to detect falls. The aforementioned methods [22,23,24,25,26,27] do not consider the effect of personalization on fall detection. Medrano et al [8] evaluated four algorithms (nearest neighbor (NN), local outlier factor (LOC), one-class support vector machine (One-Class SVM), and SVM) after personalization used as fall detectors to boost their performance when compared to their non-personalized versions.…”

Section: Related Workmentioning

confidence: 99%

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A User-Adaptive Algorithm for Activity Recognition Based on K-Means Clustering, Local Outlier Factor, and Multivariate Gaussian Distribution

Zhao

Cao

2018

Sensors

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Mobile activity recognition is significant to the development of human-centric pervasive applications including elderly care, personalized recommendations, etc. Nevertheless, the distribution of inertial sensor data can be influenced to a great extent by varying users. This means that the performance of an activity recognition classifier trained by one user’s dataset will degenerate when transferred to others. In this study, we focus on building a personalized classifier to detect four categories of human activities: light intensity activity, moderate intensity activity, vigorous intensity activity, and fall. In order to solve the problem caused by different distributions of inertial sensor signals, a user-adaptive algorithm based on K-Means clustering, local outlier factor (LOF), and multivariate Gaussian distribution (MGD) is proposed. To automatically cluster and annotate a specific user’s activity data, an improved K-Means algorithm with a novel initialization method is designed. By quantifying the samples’ informative degree in a labeled individual dataset, the most profitable samples can be selected for activity recognition model adaption. Through experiments, we conclude that our proposed models can adapt to new users with good recognition performance.

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“…Using a sequence of probabilities computed through static classifiers and training the Hidden Markov Model (HMM) on them can significantly improve the performance and smoothness of the activity recognition system [50]. Tong et al [51] use the time series from human fall sequences collected using a tri-axial accelerometer worn on the upper body. A Hidden Markov Model (HMM) is trained on events just before the collision for early fall prediction.…”

Section: Ambient Assistive Technology For Indoor Fall Riskmentioning

confidence: 99%

Ambient Technology to Assist Elderly People in Indoor Risks

Yared

Abdulrazak

2016

Computers

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While elderly people perform their daily indoor activities, they are subjected to several risks. To improve the quality of life of elderly people and promote healthy aging and independent living, elderly people need to be provided with an assistive technology platform to rely on during their activities. We reviewed the literature and identified the major indoor risks addressed by assistive technology that elderly people face during their indoor activities. In this paper, we identify these risks as: fall, wrong self-medication management, fire, burns, intoxication by gas/smoke, and the risk of inactivity. In addition, we discuss the existing assistive technology systems and classify the risk detection algorithms, techniques and the basic system principles and interventions to enhance safety of elderly people.

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HMM-Based Human Fall Detection and Prediction Method Using Tri-Axial Accelerometer

Cited by 197 publications

References 18 publications

New Advances and Challenges of Fall Detection Systems: A Survey

New Advances and Challenges of Fall Detection Systems: A Survey

A User-Adaptive Algorithm for Activity Recognition Based on K-Means Clustering, Local Outlier Factor, and Multivariate Gaussian Distribution

Ambient Technology to Assist Elderly People in Indoor Risks

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