Gait analysis of smart phones with the help of the accelerometer sensor

Spor alanlarında insan hareketlerini ölçme yeteneği performans ölçüm ve gelişimi için önemli konular arasındadır. Bu durum aynı zamanda klinik değerlendirmelerin de önemli bir parçasıdır. Özellikle elektromanyetik sistemler insan hareketlerini değerlendirmek için en yaygın kullanılan yöntemler arasında yer alır. Buradaki çalışmada 100 metre uzunluğunda bir koridorda 50 farklı kişinin yürüme verileri kullanılmıştır. Yürüme verileri akıllı telefon için geliştirilen bir yazılım ile ivmeölçer sensöründen elde edilmiştir. Verilere üç boyutlu Local Binary Pattern (LBP) yöntemi uygulanmış ve toplam 768 öznitelik çıkarılmıştır. Farklı sınıflandırma algoritmaları ile testler yapılmış ve Subspace KNN ile %97,2 başarılı sınıflandırma elde edilmiştir. Cinsiyete göre yapılan sınıflandırmada ise %99,7 başarılı sınıflandırma elde edilmiştir. Bu yöntem ile yürüme bozukluğu tespitinde yüksek maliyetli cihazlar yerine daha ekonomik yöntemler geliştirileceği düşünülmektedir.The ability to measure human movements in sports fields is among the important issues for performance measurement and development. This instance is also an important part of clinical evaluations. Electromagnetic systems are among the most widely used methods to evaluate human movements. In this study, walking data of 50 different people were used in a 100-meter-long corridor. The walking dataset was obtained from the accelerometer sensor with a software developed for the smartphone. Three-dimensional Local Binary Pattern (LBP) method was applied to the dataset and a total of 768 features were generated. Datasets were made with different classification algorithms and 97.2% successful classification was achieved with Subspace KNN. In the classification according to gender, 99.7% successful classification was obtained. With this method, it is thought that more economical methods will be developed instead of high-cost devices in detecting gait disorders

Real-time identification using gait pattern analysis on a standalone wearable accelerometer

Wearable devices can gather sensitive information about their users. For this reason, automated authentication and identification techniques are increasingly adopted to ensure security and privacy. Furthermore, identification can be used to automatically customize operations according to the needs of the current user. A gait-based identification method that can be executed in real time on devices with limited resources is here presented. The method exploits a wearable accelerometer to continuously analyze the user’s gait pattern and perform identification. Experiments were conducted with 10 volunteers, who carried the device in a trouser pocket and followed their daily routine without predefined constraints. In total, ~98 hours of acceleration traces were collected in uncontrolled environment, including 3073 gait segments. User identification results show a recognition rate ranging from 95% to 100%, depending on the mode of operation. It is demonstrated that the method can be executed on a standalone device with <8 KB of RAM. In addition, the energy consumption is evaluated and compared with an architecture that requires the presence of an external computing unit. Results show that the proposed solution significantly improves the lifetime of the device (approximately +70% for the considered platform), hence fostering user acceptance