# Positioning Methods and the Use of Location and Activity Data in Forests
## 4 Location-Based Services, the Internet of Things, Wearable Technology, and Big Data
### Activity Recognition  
The concept of activity recognition refers to use of the wearable devices and sensors described in the previous section to quantify and monitor human activity, with applications ranging from fitness and health to military and security [16; 17]. Activity can be surmised from interactions between users and sensor-enabled objects such as in smart houses, or can be predicted based on measurements collected by wearable sensors [16]. Wearable sensors can detect user movement (e.g., walking, running, standing), location, or physiological traits (e.g., heart rate, respiration), as well as provide context about a user's environment (e.g., temperature, humidity, audio level, light level) [16; 18]. They facilitate continuous, real-time activity monitoring, but are easily removable when users do not want their activities recorded [17]. Smartphones are a popular choice for wearable, sensor-based activity recognition [11; 12; 13]. They are relatively unobtrusive and widely used, and they include several internal sensors. [PERSON] et al. developed activity recognition models to characterize the work activities of professional timber fallers based only on smartphone sensor data [14]. Models correctly characterized real-time activities of the loggers working on industrial cable logging operations such as felling trees with chainsaws, clearing brush, and walking between trees [14]. Smartphones typically include GNSS chips, triaxial accelerometers which record movement in three directions, gyroscopes to determine orientation, microphones that record sound or measure sound levels, cameras, light sensors, digital thermometers, and magnetometers to measure compass direction [16; 18]. Because they also have internal microcomputers, smartphones can process sensor data directly using existing or custom apps. This reduces the need to transmit primary data through a wireless network, which can slow down real-time applications considerably [16]. Smartwatches and other wrist-based sensors are also now used extensively in recreation, sports and exercise [8; 9; 10; 195]. An important consideration for employing smartphones and other wireless sensors to collect continuous sensor data, however, is device battery life, especially when multiple or energy-draining (e.g., GNSS) sensors are engaged [16; 17]. In the future, activity recognition may be achieved using different methodologies without sensor-based devices, however. For instance, [PERSON] et al. demonstrated how disruptions in WiFi signals could be used to model human movement speeds, which in turn could predict activity with 96% accuracy [196]. To predict activities, initial sensor measurements associated with a particular activity must be collected and then processed with feature extraction, which involves filtering out relevant information from the raw sensor data (Figure 13) [16; 17]. Both time and frequency domain features, such as mean, standard deviation, variance, and Fast Fourier Transform and Discrete Cosine Transform coefficients, may be extracted with a moving window approach [13; 14; 197]. Using a moving window, subsets of the data are defined by the window size (i.e., length of time) and this window is advanced in pre-determined increments. In this way, features are calculated using a new subset of the data defined each time the window advances [14]. A machine learning model is generated based on patterns in the extracted features and used to infer activity based on sensor measurements. Various machine learning algorithms can be used to develop predictive activity recognition models, such as Decision Trees, Support Vector Machines, K Nearest Neighbors, and Naive Bayes [13; 197]. Device orientation and location on the body are also important considerations that affect the accuracy of predictive models [13; 197; 198; 199]. Ideally, activity models are individual-specific (subject dependent), to account for nuances in movement among users, but for practical reasons, may generalize an activity across a group of users (subject independent) [9; 16].