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FallDroid: An Automated Smart-Phone-Based Fall Detection System Using Multiple Kernel Learning
Shahzad, Ahsan,Kim, Kiseon IEEE 2019 IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS - Vol.15 No.1
<P>Common fall occurrences in the elderly population pose dramatic challenges in public healthcare domain. Adoption of an efficient and yet highly reliable automatic fall detection system may not only mitigate the adverse effects of falls through immediate medical assistance, but also profoundly improve the functional ability and confidence level of elder people. This paper presents a pervasive fall detection system developed on smart phones, namely, FallDroid that exploits a two-step algorithm proposed to monitor and detect fall events using the embedded accelerometer signals. Comprising of the threshold-based method and multiple kernel learning support vector machine, the proposed algorithm uses novel techniques to effectively identify fall-like events (such as lying on a bed or sudden stop after running) and reduce false alarms. In addition to user convenience and low power consumption, experimental results reveal that the system detects falls with high accuracy (<TEX>$97.8\%$</TEX> and <TEX>$91.7\%$</TEX>), sensitivity (<TEX>$99.5\%$</TEX> and <TEX>$95.8\%$</TEX>), and specificity (<TEX>$95.2\%$</TEX> and <TEX>$88.0\%$</TEX>) when placed around the waist and thigh, respectively. The system also achieves the lowest false alarm rate of 1 alarm per 59 h of usage, which is best till date.</P>
Shahzad, Ahsan,Ko, Seunguk,Lee, Samgyu,Lee, Jeong-A,Kim, Kiseon IEEE 2017 IEEE SENSORS JOURNAL Vol.17 No.20
<P>Falls are a major cause of morbidity and long-term hospitalization among growing older population. An automated and accurate fall-risk assessment system is vital to identify high fall-risk population and to prevent falls by early intervention. Therefore, this paper provides an objective, cost-effective, and unsupervised method to obtain functional balance and mobility assessment-based fall-risk of community-dwelling older adults. More specifically, waist-mounted triaxial accelerometer signals acquired from directed routine (supervised control movements) are used to estimate the well-known clinical assessment score-Berg balance scale (BBS). The trunk acceleration signals are used to extract features and to find the optimal subset of features for each training data during repeated tenfold cross validation of the BBS estimation model. The average of two BBS estimates based on test and retest yielded a strong correlation rho = 0.86 with the standard BBS score. Also, high correlation (rho = 0.90) and low root-mean-square error (1.66) was observed between the two estimates of each subject. The proposed method is well suited for the assessment of balance impairment and pre-screening of quantitative fall-risk in an unsupervised setting. It has the potential to act as a surrogate of the standard clinical assessment-BBS.</P>
Nguyen, Minh Tuan,Shahzad, Ahsan,Nguyen, Binh Van,Kim, Kiseon Elsevier 2018 Biomedical Signal Processing and Control Vol.44 No.-
<P>Sudden cardiac arrest is mainly caused by ventricular fibrillation and ventricular tachycardia, which are known as shockable rhythms. In this paper, a detection algorithm of shockable rhythms including support vector machine (SVM) model uses the public electrocardiogram (ECG) databases for training and testing. The databases are the Creighton University Ventricular Tachyarrhythmia Database (CUDB) and the MIT-BIH Malignant Ventricular Arrhythmia Database (VFDB). At first, to compose a set of good features, we extend a well-known set of 2 good features such as Count2 and VF-filter Leakage Measure (Lk). We supplemented 5 more good features, selected based on a binary genetic algorithm-based feature selection, among 11 new input candidate features. All the combinations of 7 good features are estimated for their performance on the training and the testing data using the SVM models to identify 6 combinations of the final feature pool. 5-Folds cross validation is then implemented carefully to validate the performance of the SVM classifier using final feature pool on separated and entire 5s-segment databases. The final combination of 4 features, which includes Count2, Lk, Threshold Crossing Interval (TCI), and Centroid Frequency (CF), is addressed by the highest validation performance of the corresponding SVM model. The Count2 shows the proportion of the signal, which is above the mean absolute values of the output of an integer coefficient recursive bandpass filter computed for every 1 s time interval. The Lk represents the output of a narrow bandstop filter, which is applied to the ECG signal with the central frequency being the mean signal frequency. The TCI shows the average time between the fixed thresholds, which are computed for every] s-segment using the pulses converted from the ECG signal. The CF is the frequency, which bisects vertically the area under the power spectrum. For the proposed algorithm, the average accuracy of 95.9%, sensitivity of 91.7%, and specificity of 96.8% are archived on the evaluation data of the entire database. Comparing to the performance of the SVM model using a combination of the Count2 and the Lk, we report a significant improvement for the accuracy of the SVM model using the final feature combination in average, i.e. 2.6%, 22.4%, and 2.7% on the evaluation data of the entire database, the CUDB, and the VFDB, respectively. Furthermore, existence of ventricular ectopic beats in the input data shows a negligible influence on the final performance of classification. (C) 2018 Published by Elsevier Ltd.</P>