# Adaptive Learning Approach for Human Activity Recognition Using Data from Smartphone Sensors
## 1 Introduction  
Many different healthcare and entertainment applications have been created, adapting human activity recognition in smartphones with embedded sensors. Activity recognition is especially relevant for fitness, running, and other sports. A lot of smartphone users track their activity and how long they are active, count their steps, calculate burnt calories, detect falls or posture, and much more. Various apps have been created for such purposes. Initially, several wearable sensors were used to identify various physical activities for the aforementioned applications. However, because smartphones contain a variety of sensors, research has moved to these types of devices in recent years [1, 2, 3, 4, 5, 6, 7]. Smartphones are useful for human activity recognition because they come with a combination of sensors, including accelerometers and magnetic field sensors, gyroscopes, magnetometers, GPSs,and so on. Recently, many researchers started using smartphones to explore human activity recognition and algorithms used for learning various human activities [8]. For example, experiments with human activity recognition using cell phone accelerometers were performed in ref. [3]. In the paper, the extracted features from accelerometers were used with the following classification techniques: Multilayer Perceptron, Simple Logistic, Random Forest, LMT, SVM, and LogitBoost. These techniques showed good recognition accuracy in relation to walking, jogging, standing, and sitting, whereas climbing upstairs and downstairs were poorly recognized. A survey in ref. [9] analyzed activity recognition with smartphone sensors such as an accelerometer, ambient temperature sensor, light sensor, magnetometer, proximity sensor, barometer, humidity sensor, gyroscope, etc. Several categories of activities were analyzed, including simple activities like walking, jogging, standing or sitting; more complex activities like taking buses, shopping, and driving a car; and other categories of activities such as living, working, and health-related activities. The authors listed the major, common challenges for activity recognition using mobile sensors: subject sensitivity, location sensitivity, activity complexity, energy and resource constraints, and insufficient training sets. The secure client-server architecture proposed in ref. [10] allowed for the creation of a real-time human activity recognition system. A K-nearest neighbors (KNN) algorithm was used for activity recognition. Up to 100% of recognition accuracy for running and walking activities was achieved. Also, the overall accuracy of the model reached up to 95%. Deep learning is also used in various studies of activity recognition, such as ref. [11]. Deep learning models require a lot of available data and computing resources, which are not available for wearable devices. Moreover, these models are often trained offline, which cannot be executed in real-time. A survey of using Hidden Markov models (HMMs) in human activity recognition was performed in ref. [12]. A continuous HMM and discrete HMM were proposed as a hierarchical probabilistic model to recognize a user's activities in smartphones. A separate HMM was used to model a single activity. In ref. [13], the authors proposed a user adaptation technique to improve a human activity recognition system based on an HMM. Several different physical activities (walking, walking upstairs, walking downstairs, sitting, standing, and lying down) were modeled. As reported, the experimental results showed a significant error rate reduction. Various recognition algorithms can be divided into three parts by the nature of learning patterns in data. The first type is offline algorithms, with the classifiers trained solely before starting to use them. The second type is adaptive learning (also referred as online) algorithms, which are trained in real-time with incoming data as they are being used [14; 15].