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Human Activity Recognition Methods - What They Are And How They Work

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Human activity recognition methods are utilized to identify and assess human postures and activities.

Technological breakthroughs have put in motion a slew of unparalleled marvels to make our lives easier.

Image detection, computer vision, and face recognition are pioneering methods that enable human activity recognition.

Although the full potential of these methods has yet to be realized, it has already proven effective in various domains such as sports training, security, entertainment, ambient-assisted living, and health monitoring and management.

Human position estimate has gained popularity because of its utility and adaptability.

COPYRIGHT_WI: Published on https://washingtonindependent.com/ebv/human-activity-recognition-methods/ by Jaya Mckeown on 2022-08-29T23:32:48.038Z

What Is Human Activity Recognition?

The method of analyzing human motion using computer and machine vision technologies is known as human activity recognition.

A human motion may be defined as actions, gestures, or behaviors captured by sensors.

After that, the movement data is converted into action instructions that computers may use to execute and evaluate human activity recognition code.

Human activity recognition is a large topic of research that focuses on recognizing a person's individual movement or action based on sensor data.

Movements are common indoor activities such as walking, conversing, standing, and sitting.

They might also be more concentrated tasks, such as those conducted in a kitchen or on a manufacturing floor.

Sensor data, such as video, radar, or other wireless means, may be captured remotely.

Data may also be collected directly on the subject by carrying specialized gear or smartphones equipped with accelerometers and gyroscopes.

Human Activity Recognition Categories

There are various categories for human activity recognition, as detailed as follows.

Visual Activity Recognition

It employs visual sensing capabilities, such as camera-based surveillance systems to monitor an actor's activity and changes in its surroundings.

It consists of four steps: human detection, behavior monitoring, activity identification, and high-level activity assessment.

Image-based techniques employ single or several cameras to rebuild the 3D human stance.

By separating the human body from the backdrop, picture analysis is feasible. This is accomplished using a background subtraction algorithm that adjusts to environmental changes.

Activity Recognition Using Sensors

It employs sensor network technology to monitor an actor's behavior and surroundings. In this situation, sensors are connected to people.

Data from sensors is gathered and processed using data mining or machine learning methods to create activity models and recognize activities.

Recognized activities include human body motions such as walking, running, and sitting down/up.

Because of their size or battery life, most wearable sensors are unsuitable for real-world applications.

Wearable sensors or object-attached sensors may be used in a sensor-based strategy.

Taxonomy of Human-Sensing Devices

Under the broad umbrella of "human sensing," classify the process of obtaining information about humans in any situation.

This only applies to the inference of spatiotemporal properties.

These low-level components describe the location and history of persons in a given setting.

How Is Human Activity Recognition Processed?

The majority of research on human activity identification begins with the assumption of a figure-centric setting with a blank background.

Complex acts may often be simplified into more manageable ones by breaking them down into component parts.

Because individuals are driven by their routines, identifying the primary activity that underlies a movement may be difficult.

Another challenging task is constructing a real-time visual model to study and gain knowledge of human emotions.

Human activity recognition aims to evaluate activities shown in video sequences or still photographs.

To address these concerns, a job that consists of the following three components is required:

  • Background subtraction is the process by which the computer seeks to differentiate between the components of a picture that remain static throughout time (the background) and the elements of the image that shift or transform (foreground)
  • Human tracking, the system can find human motion over time.
  • Based on their level of complexity, they may be categorized as gestures, atomic actions, human-to-object interactions, group actions, behaviors, and events.

The movements of a person's body parts that are considered to be gestures are considered basic motions that may connect to the activities of a particular individual.

Atomic actions are a person's movements that describe a specific movement that may be a component of the broader activity.

A person may perform these motions.

Interactions between two or more people or objects may be classified as human-to-object or human-to-human interactions.

Activities that a group of persons carries out are group actions.

Human behaviors are the physical acts associated with a person's feelings, personality, and psychological state.

Security cameras and electronic devices
Security cameras and electronic devices

Unimodal Human Activity Recognition Methods

Unimodal human activity identification methods are techniques for identifying human activities from data of a single modality.

Most current techniques depict human activities as a collection of visual characteristics derived from video sequences or still photographs.

To determine the underlying activity label, multiple classification algorithms are used.

The following unimodal techniques are suitable for detecting human activities based on motion characteristics.

Space-time methods

Space-time methods are aimed at identifying activities based on space-time properties or trajectory matching.

A large family of methods is based on optical flow, which has proven to be a helpful hint.

Real-time action classification and prediction examine actions as three-dimensional space-time shadows of moving people.

Stochastic methods

Researchers have developed and used many stochastic processes to derive meaningful results for human activity recognition.

Each action is represented by a feature vector containing information about location, velocity, and local descriptors.

These approaches assume that the human body's contents can provide evidence of the underlying activity.

A soccer player, for example, interacts with a ball while playing the game.

Human behaviors are frequently associated with the actor who performs a specific action.

Rule-based methods

Rule-based methods represent an activity by identifying repeating occurrences using rules or characteristics that characterize an event.

Each activity is viewed as a set of basic rules/attributes, allowing the development of a descriptive model for identifying human activities.

Each subject must follow a set of rules while participating in an activity.

Complex human behaviors cannot be identified directly using rule-based techniques.

A breakdown into more minor atomic actions is used to recognize complicated or concurrent activities, and then the combination of individual steps is used.

Shape-based methods

Researchers have shown a strong interest in modeling human stance and appearance over the past several decades.

Parts of the human body are represented in 2D space as rectangular patches and in 3D space as volumetric forms.

Many algorithms offer a wealth of information on how to solve this problem. Graphical models are widely used in 3D human posture modeling.

Human posture estimation is improved by combining discriminative and generative models.

Using multiview pictorial structural models poses from various sources were projected into 3D space.

Incorporating the pose-specific and joint appearance of body parts contributes to a more compelling depiction of the human body.

The human skeleton is divided into five sections, with each unit used to train a hierarchical neural network.

The human pose is represented using a hierarchical graph and dynamic programming.

The recognition procedures could be implemented in real-time using the incremental covariance update and on-demand closest neighbor classification techniques.

The resulting posture predictions are heavily utilized in action recognition.

Human posture estimation is extremely sensitive to environmental factors such as light changes, viewpoint variations, occlusions, backdrop clutter, and human clothing.

Low-cost technologies, such as Microsoft Kinect and other RGB-D sensors, may exploit these constraints effectively and provide a reasonably accurate estimate.

Multimodal Human Activity Recognition Methods

A slew of variables can be used to shed light on the specifics of an activity.

Features can be fused early or late in several multimodal techniques, called feature fusion.

Concatenating numerous features into one conspicuous feature vector and then learning the underlying action is the most straightforward way to gain the benefits of multiple features.

Although the recognition performance of this feature fusion approach is improved, the resulting feature vector has a much larger size.

A linkage between the underlying event and the multiple modalities must be established to understand the data.

Audio-visual analysis can be used in various ways, such as for audio-visual synchronization, tracking, and activity identification, among many others.

Effective Methods

Emotional computing research attempts to model a person's ability to express, understand, and regulate their effective emotional states.

A fundamental difficulty in affective computing is the accurate labeling of data.

Creating accurate and ambiguous effective models may be harmed by preprocessing emotional annotations.

Activation, expectancy, power, and valence are the four essential emotional aspects addressed.

Late fusion is used to combine audio and visual data in this method.

Although this system could accurately identify a person's emotional state, the amount of computer power required was substantial.

It is possible to extract and select the most useful multimodal properties using deep learning to model emotional expressions quickly.

Behavioral Methods

Methods based on behavior aim to identify nonverbal multimodal signs, such as body language, facial expressions, and auditory cues.

A behavior recognition system can discover a person's personality and mental health.

Video surveillance and human-computer interaction are only two of the many uses of this technology.

Video sequences' auditory information is fed into a system that can detect human activity.

However, this approach has a significant flaw because it relies on separate classifiers for learning the aural and visual contexts.

Social Networking Methods

Social interactions are important in daily life. Interaction with other people through their activities is essential to human behavior.

People modify their behavior in reaction to the group around them when they engage in social contact.

Most social networking platforms that influence people's behavior, such as Facebook, Twitter, and YouTube, track social connections and infer how such sites may be involved in issues of identity, privacy, social capital, youth culture, and education.

Furthermore, the study of social interactions has aroused the interest of scientists, who seek to gain important insights into human behavior.

A new human behavior recognition evaluation provides a complete overview of the most recent automated human behavior analysis methodologies for single-person, multi-person, and object-person interactions.

Multimodal Feature Fusion

Consider the following scenario: numerous people are engaged in a specific activity/behavior, and some are making noises.

A human activity identification system may detect the underlying action utilizing visual input in the most basic instance.

However, the audio-visual analysis may increase identification accuracy because people may exhibit different activities with similar body motions but different sound intensity levels.

The audio data may help determine the subject of interest in a test video sequence and discriminate between other behavioral states.

In multimodal feature analysis, the dimensionality of data from several modalities poses a considerable problem.

For example, visual characteristics are significantly more sophisticated and have better dimensions than audio.

Video-Based Human Action Recognition using Deep Learning

People Also Ask

What Is Human Activity Recognition Using Smartphones?

Human activities recognition is the classification of a person's activity utilizing sensitive sensors impacted by human movement.

Smartphone users and capabilities (sensors) are increasing, and users often take their smartphones with them.

Why Is Human Activity Recognition Needed?

Human activity recognition is crucial in human-to-human contact and interpersonal relationships.

It is tough to extract since it contains information about a person's identity, personality, and psychological condition.

What Is Human Activity Recognition System?

Human activity recognition is a vast topic of research concerned with recognizing a person's action based on sensor data.

Movements are common indoor activities such as walking, conversing, standing, and sitting.

Which Algorithms Use Human Activity Recognition?

Markov Chain model is crucial in the activity recognition system. It is used to identify gestures, words, and patterns.

This model is used in activity recognition to classify data and temporal occurrences.

Conclusion

Human activity recognition gathers events to identify a series of annotations to recognize human behavior to establish the ecological state.

Human activity can be identified based on a single movement. Humans naturally tend to pay greater attention to dynamic items than static ones.

Human activity recognition analysis is one of the most active study fields in machine learning right now.

The approach varies from typical algorithms using hand-crafted heuristically derived features to produce freshly generated hierarchical self-evolving features.

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About The Authors

Jaya Mckeown

Jaya Mckeown - Jaya moved to Boston from New York to pursue a master's degree in corporate communications at Emerson College. This experience, combined with her undergraduate degree in psychology and teaching, has equipped her with valuable skills that she employs on a daily basis in real estate negotiations, homebuyer and seller education, and successful promotion of the team's listings. Jaya's clients often characterize her as meticulous, proactive, and enjoyable to be around.

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