Online personal risk detection based on behavioural and physiological patterns

Barrera-Animas, Ari Yair; Trejo, Luis A.; Medina‐Pérez, Miguel Angel; Monroy, Raúl; Camiña, J. Benito; Godínez, Fernando

doi:10.1016/j.ins.2016.08.006

Cited by 16 publications

(32 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The aim of this study is to improve the accuracy of the detection results reported by Barrera-Animas et al [1]. To accomplish this, a new algorithm is proposed and its applicability to the personal risk detection problem demonstrated.…”

Section: Introductionmentioning

confidence: 97%

“…Personal risk detection [1] has been defined as the timely identification of when someone is in a dangerous situation, such as a health crisis or car accident or other events that may endanger a person’s physical integrity. To structure this problem, Barrera-Animas et al [1] suggested that people usually behave according to the same behavioral and physiological patterns or small variations of them.…”

Section: Introductionmentioning

confidence: 99%

“…To structure this problem, Barrera-Animas et al [1] suggested that people usually behave according to the same behavioral and physiological patterns or small variations of them. A risk-prone situation should produce sudden and significant deviations in these user patterns, and the changes can be captured by a group of sensors, such as an accelerometer, gyroscope and heart rate monitor, which are normally found in current wearable devices.…”

Section: Introductionmentioning

confidence: 99%

“…A risk-prone situation should produce sudden and significant deviations in these user patterns, and the changes can be captured by a group of sensors, such as an accelerometer, gyroscope and heart rate monitor, which are normally found in current wearable devices. The problem posed in [1] is an anomaly detection problem, where the aim is to distinguish an unusual condition (possibly a risk-prone situation) from a normal behavior. One-class classification mechanisms have proven effective in this context.…”

Section: Introductionmentioning

confidence: 99%

“…One-class classification mechanisms have proven effective in this context. Indeed, Barrera-Animas et al reported in [1] that a one-class support vector machine (ocSVM) achieved the best performance and best average ranking compared with other classifiers in tests.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Ensemble of One-Class Classifiers for Personal Risk Detection Based on Wearable Sensor Data

Rodríguez

Barrera-Animas

Trejo

et al. 2016

Sensors

Self Cite

View full text Add to dashboard Cite

This study introduces the One-Class K-means with Randomly-projected features Algorithm (OCKRA). OCKRA is an ensemble of one-class classifiers built over multiple projections of a dataset according to random feature subsets. Algorithms found in the literature spread over a wide range of applications where ensembles of one-class classifiers have been satisfactorily applied; however, none is oriented to the area under our study: personal risk detection. OCKRA has been designed with the aim of improving the detection performance in the problem posed by the Personal RIsk DEtection(PRIDE) dataset. PRIDE was built based on 23 test subjects, where the data for each user were captured using a set of sensors embedded in a wearable band. The performance of OCKRA was compared against support vector machine and three versions of the Parzen window classifier. On average, experimental results show that OCKRA outperformed the other classifiers for at least 0.53% of the area under the curve (AUC). In addition, OCKRA achieved an AUC above 90% for more than 57% of the users.

show abstract

Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations