Activity recognition
Activity recognition aims to recognize the actions and goals of one or more agents from a series of observations on the agents' actions and the environmental conditions. Since the 1980s, this research field has captured the attention of several computer science communities due to its strength in providing personalized support for many different applications and its connection to many different fields of study such as medicine, human-computer interaction, or sociology.
Due to its multifaceted nature, different fields may refer to activity recognition as plan recognition, goal recognition, intent recognition, behavior recognition, location estimation and location-based services.
Types
Sensor-based, single-user activity recognition
-based activity recognition integrates the emerging area of sensor networks with novel data mining and machine learning techniques to model a wide range of human activities. Mobile devices provide sufficient sensor data and calculation power to enable physical activity recognition to provide an estimation of the energy consumption during everyday life. Sensor-based activity recognition researchers believe that by empowering ubiquitous computers and sensors to monitor the behavior of agents, these computers will be better suited to act on our behalf. Visual sensors that incorporate color and depth information, such as the Kinect, allow more accurate automatic action recognition and fuse many emerging applications such as interactive education and smart environments. Multiple views of visual sensor enable the development of machine learning for automatic view invariant action recognition. More advanced sensors used in 3D motion capture systems allow highly accurate automatic recognition, in the expenses of more complicated hardware system setup.Levels of sensor-based activity recognition
Sensor-based activity recognition is a challenging task due to the inherent noisy nature of the input. Thus, statistical modeling has been the main thrust in this direction in layers, where the recognition at several intermediate levels is conducted and connected. At the lowest level where the sensor data are collected, statistical learning concerns how to find the detailed locations of agents from the received signal data. At an intermediate level, statistical inference may be concerned about how to recognize individuals' activities from the inferred location sequences and environmental conditions at the lower levels. Furthermore, at the highest level, a major concern is to find out the overall goal or subgoals of an agent from the activity sequences through a mixture of logical and statistical reasoning.Sensor-based, multi-user activity recognition
Recognizing activities for multiple users using on-body sensors first appeared in the work by ORL using active badge systems in the early 1990s. Other sensor technology such as acceleration sensors were used for identifying group activity patterns during office scenarios. Activities of Multiple Users in intelligent environments are addressed in Gu et al. In this work, they investigate the fundamental problem of recognizing activities for multiple users from sensor readings in a home environment, and propose a novel pattern mining approach to recognize both single-user and multi-user activities in a unified solution.Sensor-based group activity recognition
Recognition of group activities is fundamentally different from single, or multi-user activity recognition in that the goal is to recognize the behavior of the group as an entity, rather than the activities of the individual members within it. Group behavior is emergent in nature, meaning that the properties of the behavior of the group are fundamentally different than the properties of the behavior of the individuals within it, or any sum of that behavior. The main challenges are in modeling the behavior of the individual group members, as well as the roles of the individual within the group dynamic and their relationship to emergent behavior of the group in parallel. Challenges which must still be addressed include quantification of the behavior and roles of individuals who join the group, integration of explicit models for role description into inference algorithms, and scalability evaluations for very large groups and crowds. Group activity recognition has applications for crowd management and response in emergency situations, as well as for social networking and Quantified Self applications.Approaches
Activity recognition through logic and reasoning
Logic-based approaches keep track of all logically consistent explanations of the observed actions. Thus, all possible and consistent plans or goals must be considered. Kautz provided a formal theory of plan recognition. He described plan recognition as a logical inference process of circumscription. All actions and plans are uniformly referred to as goals, and a recognizer's knowledge is represented by a set of first-order statements, called event hierarchy. Event hierarchy is encoded in first-order logic, which defines abstraction, decomposition and functional relationships between types of events.Kautz's general framework for plan recognition has an exponential time complexity in worst case, measured in the size of the input hierarchy. Lesh and Etzioni went one step further and presented methods in scaling up goal recognition to scale up his work computationally. In contrast to Kautz's approach where the plan library is explicitly represented, Lesh and Etzioni's approach enables automatic plan-library construction from domain primitives. Furthermore, they introduced compact representations and efficient algorithms for goal recognition on large plan libraries.
Inconsistent plans and goals are repeatedly pruned when new actions arrive. Besides, they also presented methods for adapting a goal recognizer to handle individual idiosyncratic behavior given a sample of an individual's recent behavior. Pollack et al. described a direct argumentation model that can know about the relative strength of several kinds of arguments for belief and intention description.
A serious problem of logic-based approaches is their inability or inherent infeasibility to represent uncertainty. They offer no mechanism for preferring one consistent approach to another and are incapable of deciding whether one particular plan is more likely than another, as long as both of them can be consistent enough to explain the actions observed. There is also a lack of learning ability associated with logic based methods.
Another approach to logic-based activity recognition is to use stream reasoning based on answer set programming, and has been applied to recognising activities for health-related applications, which uses weak constraints to model a degree of ambiguity/uncertainty.
Activity recognition through probabilistic reasoning
and statistical learning models are more recently applied in activity recognition to reason about actions, plans and goals under uncertainty. In the literature, there have been several approaches which explicitly represent uncertainty in reasoning about an agent's plans and goals.Using sensor data as input, Hodges and Pollack designed machine learning-based systems for identifying individuals as they perform routine daily activities such as making coffee. Intel Research Lab and University of Washington at Seattle have done some important works on using sensors to detect human plans. Some of these works infer user transportation modes from readings of radio-frequency identifiers and global positioning systems.
The use of temporal probabilistic models has been shown to perform well in activity recognition and generally outperform non-temporal models. Generative models such as the Hidden Markov Model and the more generally formulated Dynamic Bayesian Networks are popular choices in modelling activities from sensor data.
Discriminative models such as Conditional Random Fields are also commonly applied and also give good performance in activity recognition.
Generative and discriminative models both have their pros and cons and the ideal choice depends on their area of application. A dataset together with implementations of a number of popular models for activity recognition can be found .
Conventional temporal probabilistic models such as the hidden Markov model and conditional random fields model directly model the correlations between the activities and the observed sensor data. In recent years, increasing evidence has supported the use of hierarchical models which take into account the rich hierarchical structure that exists in human behavioral data. The core idea here is that the model does not directly correlate the activities with the sensor data, but instead breaks the activity into sub-activities and models the underlying correlations accordingly. An example could be the activity of preparing a stir fry, which can be broken down into the subactivities or actions of cutting vegetables, frying the vegetables in a pan and serving it on a plate. Examples of such a hierarchical model are Layered Hidden Markov Models and the hierarchical hidden Markov model, which have been shown to significantly outperform its non-hierarchical counterpart in activity recognition.