Current Techniques In Human Activity Recognition Systems
Human Activity Recognition systems have been an important area of research in field of human behavior analysis recently. Researchers have been applying many machine learning techniques to study different simple and complicated activities. Ambulant activities like, sleeping, walking, jogging, sitting, walking upstairs, walking downstairs are monitored and useful feedback is provided by HAR system. For elderly citizens it is used to help in detecting various illness, fall detection, rehabilitation. HAR systems are also used in entertainment, gaming and surveillance in smart homes. HAR framework captures activities such as ambulatory, postural, different human actions and body movements by using variety of sensors of different modalities. These sensors include wearable motion sensors, video cameras, acoustic sensors, RADAR, Echo, Wifi, magnetic sensors and infra red motion detectors and so on.
Recent alternative proposed methods are use of social network methods that utilize information from various social network sites to understand human behavior and wireless signal based human activity recognition (which utilizes signal produced by the wireless devices to categorize human activity. Despite the presence of different methods, use of sensor data from wearable devices and smartphones is a major research area in activity recognition, detection and monitoring because of its advantages in comparison to other sensor modalities. Mobile phones and wearable sensors have obvious advantages over other methods in HAR due to their unobtrusiveness, ubiquity, cost effectiveness and ease of usability. Video camera based HAR system are widely used for security purposes but they have issues related to privacy, fixed installation, space constraints and capture non target information. This system’s performance is also dependent on variable lighting conditions and suffers due to visual disturbances.
Method of feature extraction in video sensors is Spatio-temporal interest Point (STIP), Histogram of Oriented Gradient (HOG) and Region of Interest (ROI), in comparison to statistical and frequency based features used by mobile sensors to track activities which have better computational time and less complexity. Modern smartphones collect data of daily human activities from a wide range of sensors enabling researchers to further the research in field of HAR. Smartphones are now equipped with multi sensor system having sesnsors such as accelerometer, gyroscope, magnetometer, Bluetooth, proximity, light, wifi, microphones and cellular radio sensors. Accelerometers, gyroscope magnetometer group is also referred as motion sensors and they provide us informative data to track movement and recognition of activities of daily living (ADL). The motion sensors when grouped with GPS and heart rate can be used for coarse grain and content activity recognition, social interaction of users and their location. Proximity and light sensors in cellphone can also deploy if user is in dark or light place.
For healthy living in elderly citizens and their assisted living barometers, thermometers, air humidity and pedometers sensors are being used to check ambient conditions and used for preventive measures. Recently Apple has launched their new smartwatch which is equipped with an electrocardiogram (ECG) monitor which can detect low heart rate and irregular heart rhythm. Despite the above mentioned advantages of wearable and mobile sensors and development of various existing and new sensors, the wearable and mobile sensors have also been facing various challenges like intraclass variability, class imbalance, interclass similarity, deciding precise start and finish time of each activity heterogeneities across the sensing devices, and device positioning. Intraclass variations are significant when data collected by many users for same activity is not similar. Interclass variability arises when data from two different activities such as jogging and running is similar to each other. When comparing two activities like walking and jogging and former is of more duration than later class imbalance may occur and while continuous monitoring of user it is difficult to decide the exact start and end time of one activity.
Researchers are facing challenges in HAR domain because of difficulty in classifying different activities. Before classifying, various phases like preprocessing, data segmentation, identification and extraction of discriminative features are applied to data. For preprocessing, methods such as non linear, low and high pass filter, Laplacian and Gaussian filter are used. To achieve sensor data segmentation methods used are sliding windows, event or energy based activities. Segmented data is next used to extract relevant feature vectors and in order to recognize the activities, these features are further reduced through feature selection methods to select the most differentiating features. With the application of various dimensionality reduction methods, the dimension of extracted features are reduced to increase the computational efficiency. In HAR widely used such methods are principal component analysis (PCA), linear discriminate analysis (LDA) and empirical cumulative distribution functions (ECDF). Traditionally at this stage the processed data and extracted features are now applied to various machine learning or pattern recognition methods. These machine learning techniques include the Support Vector Machine, Decision Tree, naive Bayes, Hidden Markov Model, K-Nearest Neighbour (KNN) and Gaussian Mixture Model.
Challenges with machine learning techniques and evolution of deep learning methods. In HAR, the conventional pattern recognition (PR) approaches with machine learning algorithms has made significant progress and in some controlled environments, have achieved satisfying results. These methods are heuristic driven, require feature engineering from data and majorly dependent on human domain knowledge. This limits the model developed for one domain to extend to other domain. Subsequently, shallow features learned by these approaches will lead to weak performance for incremental and unsupervised tasks and not effective in capturing complex activities consisting of a series of several microactivities.
In contrast to the traditional PR methods, recently developed deep learning methods are able to learn high level meaningful feature by exercising end to end neural network. Moreover these methods such as convolutional neural network (CNN) is able to recognize complex activities and more apt to process unsupervised and incremental learning.
