# HMM Adaptation for Improving a Human Activity Recognition System
## 2 Background  
HAR systems can be categorized according to the sensor type or the machine learning algorithm. According to the sensor type, it is possible to consider various types of sensors: on-body, object-placed or ambient sensors. On example of environment sensors is video cameras in monitoring areas [1; 2]. Human activity can be also analyzed based on a rich variety of acoustic events. The determination of both the identity of sounds and their position in time may help to detect and describe that human activity [3]. Most environment sensors require an important infrastructure support: for example, the installation of video cameras in the monitoring areas. Additionally, people not always spend all their time in the same environment. In this respect, environmental sensors are limited by their infrastructure and cannot provide monitoring outside the specific environment. This limitation can be overtaken using on-body sensors [4; 5]. Body-worn sensors add new possibilities to the human monitoring system [6]: not only by being able to measure body signals (e.g., physiological, motion, location) but also by providing portable and off-site user supervision at any location without the need of fixed infrastructure. In the literature there are different approaches for locating motion sensors in different body parts such as the waist, wrist, chest and thighs achieving good classification performance [7; 8; 9]. In [10], a chest-mounted accelerometer was used for classifying five Activities of Daily Living (ADL). However, the use of body sensors has important limitations such as the user's discomfort while wearing them (these sensors are usually uncomfortable for the common user) and energy-limited mobile devices (they do not provide a long-term solution for activity monitoring). In recent years, smartphones and smartwatches have become widespread, increasing the number of possibilities for human-centered applications. These devices include embedded built-in sensors such as microphones, dual cameras, accelerometers, gyroscopes, etc. The inertial sensors are a very interesting for monitoring ADL. These devices have important advantages [11; 12]: easy device portability, unobtrusive sensing provided by the embedded sensors and the processing power of new smartphones that allow online computation. Because of this, some works focused on HAR using smartphones have been developed [13; 14; 15; 16; 17]. HAR is a machine learning problem, where a system extracts features from sensor signals, generates a model for each activity, and classifies the next activities based on these models. In the literature, different machine learning solutions have been applied to the recognition of activities including Naive Bayes [18], Decision Trees [19], and Support Vector Machines (SVMs) [20]. In many works, several approaches are compared: for example, [PERSON] [21] uses the WEKA learning toolkit to compare the accuracy rates of several machine learning approaches: C4.5 Decision Trees, Naive Bayes, k-Nearest Neighbor, and Support Vector Machines. In [13], three learning algorithms were evaluated: Logistic Regression, J48, and Multilayer Perceptron. Hidden Markov Models is a successful modeling strategy for classifying temporal sequences. HMMs offer dynamic time warping, have clear Bayesian semantics and well-understood training algorithms. HMMs are very robust against degradation, giving the possibility to be trained on one person and to be tested on another. In the last five years, there has been an increase in the number of HAR systems based on HMMs for modeling inertial signals (Table 1).