## 1 Introduction  
Wireless wearable sensor networks are usually composed of several sensor nodes attached to human body or embedded in clothing [1, 2, 3]. The sensor nodes can monitor parameters of the body as well as its environment. Communication between the wearable sensor nodes is based on wireless data connections. So far, many applications of the wearable sensor networks have been considered in the literature. These potential applications include healthcare, localization, activity monitoring, sport, fitness, augmented reality, safety, rescue, emergency management, and many others.  
Successful use of the wearable sensor networks depends on lifetime of the battery-powered sensor nodes. In order to ensure long network lifetime, the limited energy resources of sensor nodes have to be efficiently utilized. The most energy expensive operation of wireless sensor nodes is data transmission. In contrast, data processing consumes significantly less energy. Reduction of data transmission can lead to considerable energy savings and increased network lifetime [4, 5, 6]. Thus, effective methods are needed to avoid any unnecessary data transmissions in the wearable sensor networks. The existing data reduction methods are mainly focused on wireless sensor networks that collects sensor readings, execute simple prepossessing tasks, and send the data to a sink or a base station. In contrast, the sensor network considered in this paper is composed of wearable devicesthat perform more complex tasks related to human activity recognition by using embedded machine learning algorithms.  
This paper introduces a method for reducing data transmissions in wearable sensor networks, where a cluster head node collects data from different sensors to recognize human activity in real-time. In this method, the sensor nodes are equipped with binary classifiers that allow them to decide if current sensor readings have to be transmitted to cluster head or not. According to the proposed approach, the data classifiers for sensor nodes are trained with use of machine learning algorithm. An algorithm was elaborated to prepare a training data set, which consists of data readings from sensors and assigned labels. Each label indicates if a given data reading is necessary to correctly recognize the activity of monitored person or not. These labels are determined during tests of a recognition algorithm, which is used by the cluster head node to categorize human activities.  
The proposed method was implemented in a prototype of wearable sensor network for activities recognition of persons working in a computer laboratory. The prototype was designed to recognize the human activities, such as sitting, walking, or standing. Moreover, in case of sitting person, it was also recognized where the person is sitting and if the monitor is switched on. During experiments, energy consumption of sensor nodes was measured and lifetime of the wearable sensor network prototype was analysed. Results of the experimental evaluation have enabled detailed comparison of the network lifetime for the proposed approach, and for state-of-the-art transmission reduction methods. Also, the impact of data transmission reduction on accuracy of activity recognition was analysed for the compared methods in real-world experiments.  
The paper is organized as follows. Section 2 reviews related works and discusses contribution of this paper. Section 3 presents the proposed approach to reducing data transmission in wearable sensor networks. Experiments and their results are described in Section 4. Finally, conclusions are given in Section 5.