Abstract
In today’s world, where technology is advancing every single day, new methodologies are being developed, and are brought in everyday use making our lives simpler, faster, safer, and powerful. Similarly, Human Activity Recognition (HAR) is getting more popular with all the revolutions made in the technologies. Sensor Network Technology is used in industrial applications, smart homes and system. A massive amount of data can be obtained from these sensors which are linked to the human body. Recognition of Human Activities using these sensors, and wearable technologies has been actively studied. Behavior Recognition seeks to distinguish one or more people’s activities and goals through a collection of observations on the actions and environmental conditions of the person. Health surveillance, aged treatment, and plenty of other domains can be used to automatically understand the behavioral context. An existing dataset consisting of 10 subjects (5 females, 5 males) is being used in the paper, which incorporates both young and old volunteers between 19 and 60 years of old with weights ranging from 55 to 85 kg. The dataset reflects motion data collected when subjects are engaged in 11 separate (static and dynamic) smart home activities: computer usage (1 min), telephone conversation (1 min), vacuum cleaning (1 min), book reading (1 min), TV watching (1 min), ironing (1 min), walking (1 min), exercise (1 min), cooking (1 min), drinking (20 times), hair brushing (1 min) (20 times). Most of the activities are similar because of the multi sensor environment which makes it more difficult. Using three tri axial IMU (inertial measurement unit), Magnetometer, Accelerometer, Gyroscope sensors attached to the subject area of the hand, chest, and thigh, using Machine Learning we introduced a better model to prognosticate the human activity. We have applied various machine learning classification algorithms like Random Forest Classifier, K-Nearest Neighbors, Decision Tree, Multi-layer Perceptron Classifier, Extra Tree Classifier, Ensemble Extra Trees Classifier, Label Propagation and Label Spreading. The experimental results are tabulated and analyzed, and might be effectively accustomed to recognize human activities in terms of efficiency and accuracy.
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Palimkar, P., Bajaj, V., Mal, A.K., Shaw, R.N., Ghosh, A. (2022). Unique Action Identifier by Using Magnetometer, Accelerometer and Gyroscope: KNN Approach. In: Bianchini, M., Piuri, V., Das, S., Shaw, R.N. (eds) Advanced Computing and Intelligent Technologies. Lecture Notes in Networks and Systems, vol 218. Springer, Singapore. https://doi.org/10.1007/978-981-16-2164-2_48
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