## 1 Introduction  
It is important to propagate active lifestyle, since routine physical activity has been found to have multiple benefits, such as preventing chronic diseases and increasing psychological well-being [1; 2], while prolonged inactivity has been shown to lead to an increase of chronic diseases and obesity [1; 3]. Advancement of technology has brought a surge of popularity for many activity trackers in the form of mobile phone apps or wearable systems. With these devices, users are able to keep track of their training schedule, exercises and lost calories [4]. Since this makes training more interactive and allows users to have better overview of their progress, then it often motivates the users to have a more active lifestyle and lose weight over sustained periods [5; 6; 7].  
Wearable systems are used to conveniently measure, collect and analyze the user's psychological data. This requires wearables to be small and unobtrusive, which in turn puts significant demand on reducing power consumption of the system [8]. This is also significant for real-time physical activity classification, which can be used in wearables for online activity recognition by allowing automatic recognition of the activities the user is performing [9; 10]. Real-time activity recognition providesvaluable information for improving online feedback of the activity trackers or for providing extra safety by monitoring the status of the users working in high-risk environments [11].  
Power consumption required for physical activity classification is determined by multiple different components. Some of these components are based on the processing of the acceleration values, such as sampling rate of the signal and filtering [12]. Other elements are based on classification mechanics, such as classification window length, feature calculation, and the used machine learning algorithm. While studies have explored classification mechanics such as training times of different physical activity classification algorithms [13; 14], they do not provide valuable information for real-time classification, since classifier training can be done previously on a desktop computer and later implemented into the wearable system. For classification systems working in real time, it is important to focus on processing time of the calculations the system has to do online [13; 15].  
In an earlier study, our group explored how different accelerometer sampling frequencies, classification window lengths, and the number of correlating features affect the classifier performance [16]. Few studies before have evaluated how different window lengths (commonly chosen between 1.5 s [17] and 5 s [13]) affect physical activity classification performance [15; 18], but the lack of gold standard in physical activity classification makes it difficult to compare these results [19]. It has been stated that frequencies above 20 Hz cannot be expected to arise from voluntary movement [20], but comparable performance has been reported while using lower sampling frequencies [12; 21]. Various methods have been used for feature selection, such as the Relieff algorithm [22], principal component analysis [13], or information gain [15], but not in connection with window length and sampling frequency.  
The aim of this study was to create an optimized physical activity classifier that would be suitable for implementation on real-time wearable systems. The focus was on testing various sampling frequencies, window lengths and number of features in order to reduce the power consumption, and to decrease the required memory buffer without compromising classification performance. Other classification elements were chosen based on the results of other studies with emphasis on high classification performance and low power consumption.