Browsing by Subject "Fall detection"
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Item Open Access A novel heuristic fall-detection algorithm based on double thresholding, fuzzy logic, and wearable motion sensor data(Institute of Electrical and Electronics Engineers, 2023-05-25) Barshan, Billur; Turan, M. S.We present a novel heuristic fall-detection algorithm based on combining double thresholding of two simple features with fuzzy logic techniques. We extract the features from the acceleration and gyroscopic data recorded from a waist-worn motion sensor unit. We compare the proposed algorithm to 15 state-of-the-art heuristic fall-detection algorithms in terms of five performance metrics and runtime on a vast benchmarking fall data set that is publicly available. The data set comprises recordings from 2880 short experiments (1600 fall and 1280 non-fall trials) with 16 participants. The proposed algorithm exhibits superior average accuracy (98.45%), sensitivity (98.31%), and F-measure (98.59%) performance metrics with a runtime that allows real-time operation. Besides proposing a novel heuristic fall-detection algorithm, this work has comparative value in that it provides a fair comparison on the relative performances of a considerably large number of existing heuristic algorithms with the proposed one, based on the same data set. The results of this research are encouraging in the development of fall-detection systems that can function in the real world for reliable and rapid fall detection.Item Open Access Düşme tespiti için sınıflandırma yöntemlerinin karşılaştırılması(IEEE, 2014-04) Çatalbaş, Bahadır; Yücesoy, Burak; Seçer, G.; Aslan, MuratBu bildiride giyilebilir yapıda olan ve üç boyutlu ölçüm alabilen bir ivmeölçerin çıktılarını kullanarak düşme tespiti yapan farklı algoritmaların karşılaştırılması yapılmıştır. Karşılaştırma amacıyla destek vektör makineleri, yapay sinir ağları ile elde edilen sınıflandırıcılar ve kural bazlı bir sınıflandırıcı kullanılmıştır. Sınıflandırıcıların tasarlanması ve dogrulanması amacıyla 7 farklı denekten üçer defa düşme ve düşme dışındaki günlük aktivitelere ilişkin ivmeölçer verileri toplanmıştır. Yapılan karşılaştırma sonucunda tespit doğruluğu en yüksek algoritmanın %87,76 ile destek vektör makineleri olduğu bulunmuştur. En yüksek düşme tespit oranı da %90,91 ˘ olarak kural bazlı sınıflandırıcı kullanımıyla elde edilmiştir. En yüksek özgüllük oranı %89,47 ile yine destek vektör makineleri ile elde edilmiştir.Item Open Access Fall detection and classification using wearable motion sensors(2017-08) Turan, Mustafa ŞahinEffective fall-detection systems are vital in mitigating severe medical and economical consequences of falls to people in the fall risk groups. One class of such systems is wearable sensor based fall-detection systems. While there is a vast amount of academic work on this class of systems, the literature still lacks effective and robust algorithms and comparative evaluation of state-of-the-art algorithms on a common basis, using an extensive dataset. In this thesis, falldetection and fall direction classification systems that use a motion sensor unit, worn at the waist of the subject, are presented. A comparison of a variety of falldetection algorithms on an extensive dataset, comprising a total of 2880 trials, is undertaken. A novel heuristic fall-detection algorithm (fuzzy-augmented double thresholding: FADoTh) using two simple features is proposed and compared to 15 state-of-the-art heuristic fall-detection algorithms, among which it displays the highest average accuracy (98:45%), sensitivity, and F-measure values. A learner version of the same algorithm (k-NN classifier-augmented tree: kAT) is developed and compared to eight machine learning (ML) classifiers based on the same dataset: Bayesian decision making (BDM), least squares method (LSM), k-nearest neighbor classifier (k-NN), artificial neural networks (ANN), support vector machines (SVM), decision tree classifier (DTC), random forest (RF), and adaptive boosting (AdaBoost). The kAT algorithm yields an average accuracy of 98:85% and performs on par with BDM, k-NN, ANN, SVM, DTC, RF, and AdaBoost, whereas LSM produces inferior results. Finally, the same eight ML classifiers are implemented for fall direction classification into four basic directions (forward, backward, right, and left) and evaluated on a reduced version of the same dataset consisting of only fall trials. BDM achieves perfect classification, followed by k-NN, SVM, and RF. BDM, LSM, k-NN, and ANN are modified to work in the presence of data from an unknown class and evaluated on the reduced dataset. In this robustness analysis, ANN and k-NN yield accuracies above 96:2%. The results obtained in this study are promising in developing real-world fall-detection systems.Item Open Access İnsan hareketlerinin vibrasyon ve PIR algılayıcıları kullanılarak sınıflandırılması(IEEE, 2012-04) Yazar, Ahmet; Çetin, A. Enis; Töreyin, B. U.Yalnız yaşayan yaşlı ve bakıma muhtaç kişilerin yere düşmelerinin kısa sürede tespit edilmesi önemli bir sorundur. Bu bildiride, sismik ve pasif kızılberisi algılayıcılar kullanarak, belirli bir alanda yere düşme olayının tespit edilebilmesi için bir yöntem önerilmiştir. Sınıflandırma amacıyla, hızlı Fourier dönüşümü, mel-frekansı kepstral katsayıları ve ayrık dalgacık dönüşümü teknikleri kullanılarak farklı öznitelik vektörleri çıkarılmıştır. Sismik işaretler, destek vektör makineleri ile ‘düşme’ ve ‘düşme değil’ olarak iki farklı sınıfa ayrılmıştır. Pasif kızılberisi algılayıcının, belirli bir alanda hareket eden bir kişiyi tespit etmesinden sonra, sismik algılayıcının ve pasif kızılberisi algılayıcının kararları tümleştirilerek kişinin düşüp düşmediği tespit edilmektedir. Önerdiğimiz sistem gerçek zamanlı olarak standart bir bilgisayarda çalışabilmektedir.Item Open Access Investigating the Performance of Wearable Motion Sensors on recognizing falls and daily activities via machine learning(Academic Press, 2022-06-30) Kavuncuoğlu, E.; Uzunhisarcıklı, E.; Barshan, Billur; Özdemir, A.T.With sensor-based wearable technologies, high precision monitoring and recognition of human physical activities in real time is becoming more critical to support the daily living requirements of the elderly. The use of sensor technologies, including accelerometers (A), gyroscopes (G), and magnetometers (M) is mostly encountered in work focused on assistive technology, ambient intelligence, context-aware systems, gait and motion analysis, sports science, and fall detection. The classification performance of four sensor type combinations is investigated through the use of four machine learning algorithms: support vector machines (SVMs), Manhattan k-nearest neighbor classifier (M.k-NN), subspace linear discriminant analysis (SLDA), and ensemble bagged decision tree (EBDT). In this context, a large dataset containing 2520 tests performed by 14 volunteers containing 16 activities of daily living (ADLs) and 20 falls was employed. In binary (fall vs. ADL) and multi-class activity (36 activities) recognition, the highest classification accuracy rate was obtained by the SVM (99.96%) and M.k-NN (95.27%) classifiers, respectively, with the AM sensor type combination in both cases. We also made our dataset publicly available to lay the groundwork for new research.Item Open Access Investigation of sensor placement for accurate fall detection(Springer, 2017) Ntanasis, P.; Pippa, E.; Özdemir, A. T.; Barshan, Billur; Megalooikonomou, V.Fall detection is typically based on temporal and spectral analysis of multi-dimensional signals acquired from wearable sensors such as tri-axial accelerometers and gyroscopes which are attached at several parts of the human body. Our aim is to investigate the location where such wearable sensors should be placed in order to optimize the discrimination of falls from other Activities of Daily Living (ADLs). To this end, we perform feature extraction and classification based on data acquired from a single sensor unit placed on a specific body part each time. The investigated sensor locations include the head, chest, waist, wrist, thigh and ankle. Evaluation of several classification algorithms reveals the waist and the thigh as the optimal locations.