2015
DOI: 10.1109/jsen.2015.2464774
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An On-Node Processing Approach for Anomaly Detection in Gait

Abstract: Abstract-A novel method is proposed for capturing deviation in gait using a wearable accelerometer. Previous research has outlined the importance of gait analysis to assess frailty and fall risk in elderly patients. Several solutions, based on wearable sensors, have been proposed to assist geriatricians in mobility assessment tests, such as the Timed Up-and-Go test. However, these methods can be applied only to supervised scenarios and do not allow continuous and unobtrusive monitoring of gait. The method we p… Show more

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Cited by 66 publications
(38 citation statements)
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“…If the example is selected for learning, the learn action updates the threshold score for anomaly detection by learning the latest set of examples, including the newly-obtained one. The anomaly score AS i for the i t h example e i in an example set is calculated as AS i = k j=1 d(e i , e j ), where e j is the j t h nearest neighbor example of e i , k is the number of nearest neighbors in the set, and d(·) is the feature distance function [13]. The feature distance between two examples e i and e j is defined as…”
Section: Air Quality Learning (Solar)mentioning
confidence: 99%
“…If the example is selected for learning, the learn action updates the threshold score for anomaly detection by learning the latest set of examples, including the newly-obtained one. The anomaly score AS i for the i t h example e i in an example set is calculated as AS i = k j=1 d(e i , e j ), where e j is the j t h nearest neighbor example of e i , k is the number of nearest neighbors in the set, and d(·) is the feature distance function [13]. The feature distance between two examples e i and e j is defined as…”
Section: Air Quality Learning (Solar)mentioning
confidence: 99%
“…AAV provides an indication of how sudden changes in acceleration happen within a stride. These measurements are commonly used for activity recognition applications and have been recently used for fall-detection and gait analysis in humans [27,28].…”
Section: Feature Extractionmentioning
confidence: 99%
“…During the last few years, several different systems for the automatic detection of the FOG have been proposed. These are based on the classification of electrical signals coming from inertial sensors properly positioned on the patient body (Lorenzi et al, 2015), (Mazilu et al, 2014), ( Bachlin et al, 2010), (Moore et al, 2013), (B. Sijobert et al, 2014), (Mazilu et al, 2013) (Cola et al, 2015), (Atallah et al, 2014). In our work, we propose the realization of two types of wearable wireless sensing systems based on MEMS accelerometers and gyroscopes, able to recognize in real time specific kinetic features associated to motion disorders typical of (but not limited to) the PD and eventually give an auditory stimulation to the patient to release the involuntary block.…”
Section: Introductionmentioning
confidence: 99%