Background: Detecting of human movements is an important task in various areas such as healthcare, fitness and eldercare. It is now possible to achieve this aim using mobile applications. These applications provide users, doctors and related persons a better understanding about daily physical activities. It can also lead to various useful habits by following the activities of the users in their daily life. In addition, dangerous actions such as the fall of elderly people or young children are identified and necessary precautions are taken as soon as possible. Classification of human motions with motion sensor data is among the current topics of study. Smart watches have these sensors built-in. Thus, it is possible to follow the activities of a user carrying only a smart watch. Methods: The purpose of this work is to detect human movements using smart watch sensor data and machine learning methods. The data are obtained from the accelerometer, gyroscope, step counter and heart rate sensors of the smart watch. The obtained data have been divided into 2 s windows and a data set containing 500 patterns for each class has been created for each class. Results and Discussion: After the features were determined, the data set to which the principal component analysis has been applied was classified by random forest, support vector machine, C4.5 and k-nearest neighbor methods, and their performances were compared. The most successful result was obtained from the random forest method.
The aim of this study is to detect transportation modes of people by using smartphone sensors. Therefore, a mobile application was developed for this purpose and global positioning system (GPS), accelerometer, and gyroscope sensor data were collected while the subjects were walking, running, biking, and travelling by bus or by car. The application was running for over 8 h. Sensor data were tagged with 12 s intervals and 2500 patterns were obtained. Eleven features were selected from the data set and machine learning methods were applied to detect transportation modes using different sensor combinations. Performances of the methods were discussed in terms of accuracy ratios. Best results were obtained from GPS, accelerometer, and gyroscope sensor combination data using logistic regression method with 99.6% accuracy rate.
The aim of this study is to explore the usage of statistical learning methods on wearable devices and realize an experimental study for recognition of human activities by using smartwatch sensor data. To achieve this objective, mobile applications that run on smartwatch and smartphone were developed to gain training data and detect human activity momentarily; 500 pattern data were obtained with 4-second intervals for each activity (walking, typing, stationary, running, standing, writing on board, brushing teeth, cleaning and writing). Created dataset was tested with five different statistical learning methods (Naive Bayes, k nearest neighbour (kNN), logistic regression, Bayesian network and multilayer perceptron) and their performances were compared.
Giyilebilir teknolojideki gelişmelerle birlikte ortaya çıkan cihazlar hızla gündelik hayatın bir parçası haline gelmiştir. Özellikle sahip oldukları algılayıcılar, bu cihazların kullanışlılığını artırmaktadır. Bu çalışmanın amacı, akıllı saatlerin sahip olduğu algılayıcılar kullanılarak insan hareketlerinin tespit edilmesidir. Bu amaçla, akıllı saatler üzerinde çalışabilen bir mobil uygulama geliştirilmiştir. Geliştirilen uygulama ile 9 farklı insan hareketi için algılayıcı verileri akıllı saat aracılığı ile toplanmış ve 4 saniyelik pencere aralıkları ile desenler oluşturulmuştur. Oluşturulan desenler 10 farklı makine öğrenmesi yöntemi ile test edilmiş ve performansları karşılaştırılmıştır.
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