Akıllı Sensör Verileri Üzerinde Makine Öğrenmesi Yöntemleri Kullanılarak İnsan Aktivitelerinin Tanımlanması

Yıl 2022, Cilt: 1 Sayı: 1, 9 - 14, 31.12.2022

Serdar Asarkaya , Emre Ünsal

Öz

Akıllı sensör teknolojilerindeki gelişmeler ve giyilebilir cihazların maliyetlerinin düşmesi sonucunda bu cihazlardan elde edilen sensör verileri kullanılarak günlük insan aktivitelerinin tanımlanmasına yönelik nesnelerin interneti tabanlı çalışmalar günümüzde önemli bir araştırma konusudur. İnsan aktivitelerinin tanımlanması sağlık, hasta takibi ve güvenlik gibi alanlarda aktiviteye bağlı sorunların çözümüne katkı sağlayabilmektedir. Bu çalışma, akıllı sensörlerden elde edilen veriler üzerinde makine öğrenmesi yöntemlerini kullanarak insan aktivitelerinin tanımlanmasını amaçlamaktadır. Çalışmada Karar Ağacı, OneVsOne ve Çok Katmanlı Algılayıcı sınıflandırıcıları ile modeller oluşturulmuş ve aktiviteleri içeren veri seti ile eğitim ve test aşamaları gerçekleştirilmiştir. Elde edilen sonuçlar karşılaştırılmış ve en iyi sonuca Çok Katmanlı Algılayıcı modeli ile ulaşıldığı görülmüştür.

Anahtar Kelimeler

Nesnelerin İnterneti, Akıllı Sensörler, Makine Öğrenmesi, Yapay zeka

Recognition of Human Activities Using Machine Learning Methods on Smart Sensor Data

Öz

As a result of the developments in smart sensor technologies and the decrease in the costs of wearable devices, internet of things-based studies for the recognition of daily human activities by using sensor data obtained from these devices is an important research topic today. The recognition of human activities can contribute to the solution of activity-related problems in areas such as health, patient follow-up, and safety. This study aims to identify human activities using machine learning methods on data obtained from smart sensors. In this study, models were created with Decision Tree, OneVsOne, and Multilayer Perceptron classifiers, and training and testing stages were carried out with the data set containing the activities. The obtained results were compared, and it was seen that the best result was achieved with the Multi-Layer Perceptron model.

Anahtar Kelimeler

Internet of Things, Smart Sensors, Machine Learning, Artificial Intelligence

Kaynakça

• Abaklıoğlu, M. (2019). The importance of internet (IOT) technology for smartcity and objects for the future cities. İstabul: Ulusal Tez Merkezi.
• Abidine, M. B., & Fergani, B. (2021). Activity Recognition From Smartphone Data Using WSVM-HMM Classification. International Journal of E-Health and Medical Communications , 20.
• Anguita, D., Ghio, A., Oneto, L., Parra, X., & Ortiz, J. (2012). Human Activity Recognition on Smartphones using a Multiclass Hardware-Friendly Support Vector Machine. Ambient Assisted Living and Home Care. , 7657. doi:https://doi.org/10.1007/978-3-642-35395-6_30
• Chai, S. S., Cheah, L. W., Goh, K. L., Chang, Y. R., Sim, K. Y., & Chin, K. O. (2021). A Multilayer Perceptron Neural Network Model to Classify Hypertension in Adolescents Using Anthropometric Measurements: A Cross-Sectional Study in Sarawak. Computational and Mathematical Methods in Medicine, 11. doi:10.1155/2021/2794888
• Choudhury, T., Consolvo, S., Harrison, B., Hightower, J., LaMarca, A., & LeGrand, L. (2008). The Mobile Sensing Platform: An Embedded Activity Recognition System. IEEE Pervasive Computing, 32-41. doi:10.1109/MPRV.2008.39
• Ferrari, A., Micucci, D., Mobilio, M., & Napoletano, P. (2021). Trends in human activity recognition using smartphones. Journal of Reliable Intelligent Environments, 189-213. doi:10.1007/s40860-021-00147-0
• Galar, M., Barrenechea, E., Fernandez, A., & Herrera, F. (2014). Enhancing difficult classes in one-vs-one classifier fusion strategy using restricted equivalence functions. FUSION 2014 - 17th International Conference on Information Fusion. . Salamanca: ResearchGate.
• Jijo, B. T., & Abdulazeez, A. M. (2021). Classification Based on Decision Tree Algorithm for Machine Learning. Journal of Applied Science and Technology Trends, 20-28. doi:10.38094/jastt20165
• Kaur, P., Kumar, R., & Kumar, M. (2019). A healthcare monitoring system using random forest and internet of things (IoT). Multimedia Tools and Applications, 19905–19916. doi:10.1007/s11042-019-7327-8
• Kurama, V. (2020, 1 1). A Complete Guide to Decision Trees. 4 18, 2022 tarihinde https://blog.paperspace.com/: https://blog.paperspace.com/decision-trees/ adresinden alındı
• Ma, J., Cheng, J., Ding, Y., Lin, C., Jiang, F., Wang, M., & Zhai, C. (2020). Transfer learning for long-interval consecutive missing values imputation without external features in air pollution time series. Advanced Engineering Informatics, 101092. doi:10.1016/j.aei.2020.101092
• Mourtzis, D., Vlachou, E., & Milas, N. (2016). Industrial Big Data as a Result of IoT Adoption in Manufacturing. Procedia CIRP, 290-295. doi:10.1016/j.procir.2016.07.038
• Muscillo, R., Conforto, S., Schmid, M., Caselli, P., & D'Alessio, T. (2007). Classification of Motor Activities through Derivative Dynamic Time Warping applied on Accelerometer Data. IEEE Engineering in Medicine and Biology Society. Conference (s. 4930-3). PubMed. doi:10.1109/IEMBS.2007.4353446
• Noriega, L. (2005, 11 17). Multilayer Perceptron Tutorial. 4 19, 2022 tarihinde http://citeseerx.ist.psu.edu/: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.608.2530&rep=rep1&type=pdf adresinden alındı
• Ortiz, J., Anguita, D., Ghio, A., Oneto, L., & Parra, X. (2012, 10 12). Human Activity Recognition Using Smartphones Dataset. UCI Machine Learning Repository: https://archive.ics.uci.edu/ml/datasets/human+activity+recognition+using+smartphones adresinden alındı
• Rokach, L., & Maimon, O. (2005, 1 1). Decision Trees. doi:10.1007/0-387-25465-X_9
• Sakacı, B. (2018). Askeri Personel İçin Akıllı Kıyafet Tasarımı. İstanbul: Yıldız Teknik Üniversitesi.
• scikit-learn. (2020, 1 1). Multiclass and multioutput algorithms. 4 20, 2022 tarihinde scikit-learn.org: https://scikit-learn.org/stable/modules/multiclass.html#multiclass-classification adresinden alındı
• Sehrawat, D., & Gill, N. S. (2019). Smart Sensors: Analysis of Different Types of IoT Sensors. 3rd International Conference on Trends in Electronics and Informatics (ICOEI) (s. 523-528). Tirunelveli, India: IEEE. doi:10.1109/ICOEI.2019.8862778
• Simplilearn. (2022, 2 21). An Overview on Multilayer Perceptron (MLP). https://www.simplilearn.com/: https://www.simplilearn.com/tutorials/deep-learning-tutorial/multilayer-perceptron adresinden alındı
• sklearn. (2021). sklearn.preprocessing.MinMaxScaler. 4 22, 2022 tarihinde scikit-learn.org: https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html adresinden alındı
• Su, X., & Ji, P. (2014). Activity Recognition with Smartphone Sensors. TSINGHUA SCIENCE AND TECHNOLOGY, 235-349. doi:10.1109/TST.2014.6838194
• Verma, Y. (2022, 4 7). One vs One, One vs Rest with SVM for multi-class classification. 4 20, 2022 tarihinde analyticsindiamag.com: https://analyticsindiamag.com/one-vs-one-one-vs-rest-with-svm-for-multi-class-classification/ adresinden alındı