Patient Fall Detection using Support Vector Machines

Doukas, Charalampos; Tragas, Philippos; Liapis, Dimitris; Yovanof, Gregory S.

doi:10.1007/978-0-387-74161-1_16

Cited by 74 publications

(48 citation statements)

References 14 publications

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“…Another research area that benefits from the utilisation of wearable sensors is health care, where people presented their work on automatic fall detection [8,36]. As expected, also in these experiments, the data collection was carried out in a controlled environment where participants imitate falling.…”

Section: Related Work 21 Action Recognition With Accelerometersmentioning

confidence: 97%

Personalised models for speech detection from body movements using transductive parameter transfer

Gedik

Hung

2017

Pers Ubiquit Comput

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We investigate the task of detecting speakers in crowded environments using a single body worn triaxial accelerometer. Detection of such behaviour is very challenging to model as people's body movements during speech vary greatly. Similar to previous studies, by assuming that body movements are indicative of speech, we show experimentally, on a real-world dataset of 3 h including 18 people, that transductive parameter transfer learning (Zen et al. in Proceedings of the 16th international conference on multimodal interaction. ACM, 2014) can better model individual differences in speaking behaviour, significantly improving on the state-of-the-art performance. We also discuss the challenges introduced by the in-thewild nature of our dataset and experimentally show how they affect detection performance. We strengthen the need for an adaptive approach by comparing the speech detection problem to a more traditional activity (i.e. walking). We provide an analysis of the transfer by considering different source sets which provides a deeper investigation of the nature of both speech and body movements, in the context of transfer learning.

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Section: Related Work 21 Action Recognition With Accelerometersmentioning

confidence: 97%

Personalised models for speech detection from body movements using transductive parameter transfer

Gedik

Hung

2017

Pers Ubiquit Comput

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“…4 can be combined with sound data classification in order to achieve improved human monitoring and activity recognition. Previous studies found in literature [30][31][32] have proved the feasibility of distinguishing movement types and indicating patient falls by utilizing solely motion data. However, the combination of sound data can improve accuracy as indicated in the following section.…”

Section: Motion and Sound Data Mergingmentioning

confidence: 99%

“…Based on previous work [30][31][32], a cascading architecture of Support Vector Machines (SVMs) has been selected for motion data classification and fall detection. SVMs is a popular algorithm for data classification into two classes [33,34] that allows the expansion of the information provided by a training data set as a linear combination of a subset of the data in the training set (support vectors).…”

Section: Advanced Data Classificationmentioning

confidence: 99%

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An assistive environment for improving human safety utilizing advanced sound and motion data classification

Doukas

2010

Univ Access Inf Soc

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This paper presents the concept and an initial implementation of an assistive awareness system that may be used for human activity interpretation and emergency recognition in cases such as elder or patient falls and distress speech expressions, thus improving their safety. Awareness is achieved through collecting, analyzing and classifying motion and sound data. The latter are collected through sensors equipped with accelerometers and microphones that are attached to the human body and transmit movement and sound data wirelessly to the monitoring unit. The detection of fall incidents has been proven to be feasible by applying Short Time Fourier Transform (STFT) and spectrogram analysis on sounds. The classification of the sound and movement data is performed using a variety of advanced classification techniques. Evaluation results provide a performance comparison between the evaluated classifiers and indicate the high accuracy and the effectiveness of the proposed implementation. The system architecture is open and can be easily enhanced to include patient awareness based on additional context (e.g., physiological data).

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“…Doukas et al [26] applied the sensor to the patient's foot in order to transmit patient movement data wirelessly to the monitoring center. The center generalizes data in the three axis and uses machine learning method to classify an event a fall or not.…”

Section: Related Workmentioning

confidence: 99%

Automatic Fall Detection using Smartphone Acceleration Sensor

Dang¹,

Truong²,

Dang³

2016

ijacsa

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Abstract-In this paper, we describe our work on developing an automatic fall detection technique using smart phone. Fall is detected based on analyzing acceleration patterns generated during various activities. An additional long lie detection algorithm is used to improve fall detection rate while keeping false positive rate at an acceptable value. An application prototype is implemented on Android operating system and is used to evaluate the proposed technique performance. Experiment results show the potential of using this app for fall detection. However, more realistic experiment setting is needed to make this technique suitable for use in real life situations.

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Patient Fall Detection using Support Vector Machines

Cited by 74 publications

References 14 publications

Personalised models for speech detection from body movements using transductive parameter transfer

Personalised models for speech detection from body movements using transductive parameter transfer

An assistive environment for improving human safety utilizing advanced sound and motion data classification

Automatic Fall Detection using Smartphone Acceleration Sensor

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