Automatic individual calibration in fall detection – an integrative ambulatory measurement framework

Liu, Jian; Lockhart, Thurmon E.

doi:10.1080/10255842.2011.627329

Cited by 17 publications

(16 citation statements)

References 25 publications

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“…However, such performance may not be maintained when the set of activities is broadened or new subjects participate in the tests. Although some studies with very high (∼100%) sensitivity and specificities exist [32,33], the performance of these algorithms degrades when implemented in the real world under realistic conditions and with new users. There are many academic works with promising results but no reliable off-the-shelf product on the market.…”

Section: Discussionmentioning

confidence: 99%

Detecting Falls with Wearable Sensors Using Machine Learning Techniques

Özdemir

Barshan

2014

Sensors

313

235

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Falls are a serious public health problem and possibly life threatening for people in fall risk groups. We develop an automated fall detection system with wearable motion sensor units fitted to the subjects' body at six different positions. Each unit comprises three tri-axial devices (accelerometer, gyroscope, and magnetometer/compass). Fourteen volunteers perform a standardized set of movements including 20 voluntary falls and 16 activities of daily living (ADLs), resulting in a large dataset with 2520 trials. To reduce the computational complexity of training and testing the classifiers, we focus on the raw data for each sensor in a 4 s time window around the point of peak total acceleration of the waist sensor, and then perform feature extraction and reduction. Most earlier studies on fall detection employ rule-based approaches that rely on simple thresholding of the sensor outputs. We successfully distinguish falls from ADLs using six machine learning techniques (classifiers): the k-nearest neighbor (k-NN) classifier, least squares method (LSM), support vector machines (SVM), Bayesian decision making (BDM), dynamic time warping (DTW), and artificial neural networks (ANNs). We compare the performance and the computational complexity of the classifiers and achieve the best results with the k-NN classifier and LSM, with sensitivity, specificity, and accuracy all above 99%. These classifiers also have acceptable computational requirements for training and testing. Our approach would be applicable in real-world scenarios where data records of indeterminate length, containing multiple activities in sequence, are recorded.

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Section: Discussionmentioning

confidence: 99%

Detecting Falls with Wearable Sensors Using Machine Learning Techniques

Özdemir

Barshan

2014

Sensors

313

235

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“…In [12], the threshold depends on user-provided parameters such as height, weight and level of activity. In [21], a real-time unit attached to the user stores ADL and performs fall detection. When fall detection is not required (at nighttime or inactivity periods), the real-time unit sends the ADL to a non-real-time unit, where the threshold is changed accordingly.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Personalization on Smartphone-Based Fall Detectors

Medrano

Plaza

Igual

et al. 2016

Sensors

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The risk of falling is high among different groups of people, such as older people, individuals with Parkinson's disease or patients in neuro-rehabilitation units. Developing robust fall detectors is important for acting promptly in case of a fall. Therefore, in this study we propose to personalize smartphone-based detectors to boost their performance as compared to a non-personalized system. Four algorithms were investigated using a public dataset: three novelty detection algorithms—Nearest Neighbor (NN), Local Outlier Factor (LOF) and One-Class Support Vector Machine (OneClass-SVM)—and a traditional supervised algorithm, Support Vector Machine (SVM). The effect of personalization was studied for each subject by considering two different training conditions: data coming only from that subject or data coming from the remaining subjects. The area under the receiver operating characteristic curve (AUC) was selected as the primary figure of merit. The results show that there is a general trend towards the increase in performance by personalizing the detector, but the effect depends on the individual being considered. A personalized NN can reach the performance of a non-personalized SVM (average AUC of 0.9861 and 0.9795, respectively), which is remarkable since NN only uses activities of daily living for training.

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“…The optimal detection threshold was determined to be −4.994, as this threshold satisfied the requirements of achieving the highest sensitivity while maintaining specificity over 95%. The effect of detection threshold on sensitivity and specificity can be found in our previous publication [12]. In particular, the sensitivity and specificity associated with this algorithm configuration were 100% and 95.65%, respectively.…”

Section: Resultsmentioning

confidence: 83%

“…As a composite score integrating both α and ω , the detection threshold is not associated with any physical meaning. The optimal detection threshold for Algorithm I was determined based on the relationship between threshold and sensitivity/specificity [12], and the following rules.…”

Section: Methodsmentioning

confidence: 99%

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Development and Evaluation of a Prior-to-Impact Fall Event Detection Algorithm

Liu

Lockhart

2014

IEEE Trans. Biomed. Eng.

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Automatic fall event detection has attracted research attention recently for its potential application in fall alarming system and wearable fall injury prevention system. Nevertheless, existing fall detection research is facing various limitations. The current study aimed to develop and validate a new fall detection algorithm using 2-D information (i.e., trunk angular velocity and trunk angle). Ten healthy elderly were involved in a laboratory study. Sagittal trunk angular kinematics was measured using inertial measurement unit during slip-induced backward falls and a variety of daily activities. The new algorithm was, on average, able to detect backward falls prior to impact, with 100% sensitivity, 95.65% specificity, and 255 ms response time. Therefore, it was concluded that the new fall detection algorithm was able to effectively detect falls during motion for the elderly population.

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Automatic individual calibration in fall detection – an integrative ambulatory measurement framework

Cited by 17 publications

References 25 publications

Detecting Falls with Wearable Sensors Using Machine Learning Techniques

Detecting Falls with Wearable Sensors Using Machine Learning Techniques

The Effect of Personalization on Smartphone-Based Fall Detectors

Development and Evaluation of a Prior-to-Impact Fall Event Detection Algorithm

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