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Human Activity Recognition: Everything You Should Know About


Trinh Nguyen

May 25, 2024

Human Activity Recognition (HAR) is a dynamic and complex field within computer science. Its widespread adoption brings significant benefits for human safety, overall well-being, and much more.

Wearable devices, for instance, monitor health parameters such as physical activity, heart rate, and sleep quality, aiding in health monitoring. In smart home environments, HAR-based solutions contribute to energy conservation and personal comfort by automatically adjusting lighting or temperature based on occupancy detection. Moreover, personal safety devices equipped with HAR capabilities can swiftly alert emergency services or designated contacts in case of emergencies. And this is just the tip of the iceberg of its potential applications.

Throughout this article, we’ll deeply discuss what human activity recognition entails, its underlying mechanisms, and its diverse implementations across various industries, leveraging the latest advancements in artificial intelligence (AI).

So, let’s dive in!

What Is Human Activity Recognition?

Human activity recognition refers to the process of using artificial intelligence to identify and name various activities based on collected raw data from different sources, such as wearable sensors, smartphone sensors, closed-circuit television (CCTV), and off-the-shelf equipment. These systems, either monitored or unsupervised, find applications in wellness, athletics, healthcare, security, sports performance, and more.

During modeling, human activity recognition models aim to predict a person’s action label from images or videos, commonly achieved through video-based or image-based action recognition. One popular approach in vision-based HAR systems is human pose estimation. Researchers are increasingly using this technique because it provides valuable insights into human behavior analysis, aiding not just HAR but also content extraction, semantic comprehension, and beyond.

Despite its utility, HAR faces significant challenges, especially when looking into specific physical attributes, cultural nuances, directions, and types of human poses, which can make distinguishing between actions like falling and doing a handstand extremely difficult. To enhance HAR systems’ accuracy and robustness, we should employ multi-modal learning and graph-based learning approaches. These methods incorporate more complex features, leverage multiple data sources, and capture spatial and temporal relationships between body parts.

How Does Human Activity Recognition Work?

How Does Human Activity Recognition Work?

A pivotal area in the scientific fields of computer vision and human activity recognition is learning how computers interpret human actions.

Here’s a breakdown of the fundamental steps involved in this process.

1. Data Collection

To begin, HAR gathers data through sensors worn or attached to users. These include accelerometers, gyroscopes, magnetometers, and GPS sensors.

Accelerometers track movement changes across three axes (x, y, z), while gyroscopes measure rotations and angular velocity. Magnetometers sense magnetic fields, and GPS sensors track users’ locations. However, GPS sensors are less common in HAR due to power consumption and limited indoor accuracy.

All this sensor data collected is typically in time-series format, with each sample representing sensor measurements at specific intervals, like every second.

2. Data Pre-processing

Data pre-processing plays a vital role in human activity recognition as it involves cleaning, transforming, and organizing raw input data to facilitate future analysis and modeling.

Several key preparation processes include:

  • Filtering: A processing technique aiming at removing noise and unwanted signals from the raw sensor data. Common filters used in HAR consist of low-pass, high-pass, and band-pass filters to suppress noise and enhance the clarity of images.
  • Feature extraction: Identifying relevant features from the sensor data based on the type of action and sensor modality. For instance, accelerometer data can be utilized to extract features like mean, standard deviation, and frequency-domain properties using techniques such as Fourier and wavelet transformations.
  • Feature selection: Choosing features to be carried out to reduce the dimensionality of the feature space. This process enhances the accuracy of activity identification algorithms by selecting the most relevant features based on their relevance to activity labeling and their correlation with other features.
  • Segmentation: Dividing sensor data into smaller segments or windows to capture the temporal aspects of activities. The size and overlap of these windows depend on the duration and intensity of the observed activities, enabling the computation of characteristics for each segment.
  • Normalization: Ensuring that features are scaled uniformly across sensors and participants by adjusting their mean and variance to 1, promoting consistency in data analysis.
  • Dimensionality reduction: Minimizing the dimensionality of the feature space and removing redundant or irrelevant features. Some dimensionality reduction techniques are Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE).
  • Missing value imputation: Filling incomplete sensor data due to device malfunctions or transmission errors. Simple methods like mean or median interpolation are often taken advantage of for this purpose.

Overall, data preprocessing is of great importance in HAR as it significantly enhances the accuracy and reliability of activity recognition models.

3. Model Selection

Next comes model selection, where various machine learning algorithms are used to recognize human activities. Your selection process should consider factors like data complexity, available resources, and performance criteria.

Followings presents some popular HAR models you should know about:

  • Decision trees offer a straightforward approach to modeling non-linear interactions between features and labels. They excel in classification tasks based on sensor data like accelerometers or gyroscope readings. Decision trees are easy to interpret and handle both continuous and categorical data. However, they may struggle with overfitting in complex or noisy datasets.
  • Random forests are ensembles of decision trees designed to handle noisy and high-dimensional data. They resist overfitting and can manage missing values efficiently. Yet, they might require more computational resources compared to individual decision trees, especially for small datasets.
  • Support Vector Machines (SVMs) stand out as robust models to handle both linear and nonlinear data. They are effective in high-dimensional spaces and less prone to overfitting. However, optimizing SVMs may require meticulous hyperparameter tuning and can be computationally demanding for large datasets.
  • Hidden Markov Models (HMMs) are statistical models widely used in HAR to recognize sequential patterns in sensor input data. They prove valuable for analyzing time-series data and complex activities with multiple steps.
  • Convolutional Neural Networks (CNNs), favored for their prowess in image processing and time-series data analysis, efficiently extract hierarchical features from raw data. While proficient in managing complex patterns, CNNs may necessitate substantial computational resources and are susceptible to overfitting.
  • Recurrent Neural Networks (RNNs) are deep learning models tailored for sequential data analysis, such as time series. They handle variable-length sequences well and can detect temporal connections in the data. Nevertheless, they may face challenges like the vanishing gradient problem and require careful initialization and regularization.

4. Model Deployment

Following model selection, human activity recognition systems are deployed using one of two methods:

  • External sensing deployment means placing external sensors, such as cameras or motion detectors, in the environment to gather information on human activities. The collected sensor data is then processed by a HAR model running on a separate computing device. This method is useful for monitoring actions in public spaces or situations where the individual being tracked cannot wear a device.
  • On-body sensing deployment requires individuals to wear sensors, such as wrist-worn accelerometers, to capture data about their activities. The sensor data is then processed either locally on the wearable device itself or remotely on a computing system. This approach is particularly effective for monitoring activities in private settings or situations where the individual being observed can wear a device.

Applications of Human Activity Recognition

Applications of Human Activity Recognition

Numerous areas have already adopted human action recognition, and new ones are constantly emerging.

Let’s go over 8 prominent HAR use cases now:

Sports Performance Analysis

Sports performance analysis benefits greatly from HAR. It aids in tracking and analyzing athletes’ movements during both competition and training sessions, identifying potential injury risks, evaluating training program efficacy, monitoring individual athlete progress, and dissecting tactical and strategic aspects of team sports.

For instance, HAR can analyze the movements of badminton players during shots, monitor runners for signs of overuse injuries, and assess soccer players’ performance during matches. It also tracks tennis players’ footwork and positioning or evaluates basketball players’ actions to enhance team defense and ball movement.

Additionally, pose tracking in AI-powered fitness applications ensures users adhere to safety guidelines and receive real-time feedback during home workouts.

Self-driving Cars

Regarding self-driving cars, HAR helps enhance safety and efficiency. By detecting pedestrians, cyclists, and other vehicles on the road, HAR enables autonomous cars to anticipate and prevent collisions.

What’s more, HAR identifies driver behaviors like hand signals and head movements, facilitating communication between self-driving vehicles and human drivers.

Human/Computer Interaction

For human-computer interaction, HAR is instrumental in determining and categorizing human gestures and movements, thereby improving the usability and accessibility of computer systems. As such, gesture-based commands for electronic devices like smartphones and smart TVs get more intuitive with HAR. Plus, HAR allows voice-based automation of computer systems, enhancing interaction with virtual personal assistants and chatbots.

On top of that, by monitoring users’ physical movements and behaviors, HAR contributes to maintaining users’ health and wellness, mitigating the adverse effects of prolonged computer use, such as eye strain and back pain.


In gaming, HAR brings numerous benefits. Recognizing and classifying player actions and gestures allows for elevating gaming experiences, making them more immersive and interactive. Motion-controlled gaming is a case in point. It utilizes HAR to translate players’ movements into fun in-game actions like swinging a sword or throwing a ball. Also, gesture-based manipulation of in-game interfaces streamlines navigation, ensuring a seamless gaming experience for players.

Furthermore, HAR can track users’ physical exertion during gameplay. Games may incentivize physical activity by rewarding players for completing steps or engaging in specific workouts.

Smart Surveillance

Smart surveillance also gains enormous advantages from HAR’s automatic video analysis capabilities, boosting security in public spaces and critical infrastructure.

By recognizing human activities such as walking, running, and loitering, HAR helps identify suspicious behavior like carrying weapons or leaving objects unattended. It also detects abnormal activity patterns, alerting security personnel promptly.

Besides, real-time HAR can identify individuals based on physical characteristics, even when faces are obscured, assisting with crowd monitoring and suspect tracking. However, the use of HAR in surveillance also raises privacy concerns, necessitating appropriate legislation and safeguards.


In the psychology field, HAR holds great promise for telehealth applications. Utilizing 3D human body poses enables remote quantitative motor assessments, facilitating healthcare delivery directly in patients’ homes.

HAR also aids in autism detection by detecting atypical behaviors, highlighting the importance of early intervention for improved quality of life.

Furthermore, human motion tracking contributes to assessing individuals’ mental states and emotional well-being, showcasing the potential of HAR in understanding human psychology.

Video Surveillance

Today, video surveillance greatly ensures public safety. Governments often deploy CCTV cameras to monitor crowd behavior, yet processing the vast visual data requires an intelligent and automated system to identify violent or suspicious activities. This is where motion-tracking technology steps in.

By tracking key points’ positions, the system can issue real-time alerts during potential assaults. Particularly, identifying individuals with raised hands or lying down might signal alarming situations, as indicated by some human pose tracking studies.

Fashion and Retail

Virtual try-on is revolutionizing fashion e-commerce, offering a modern solution to the age-old problem of buying clothes online without trying them on. Instead of relying on guesswork, customers can now visualize how garments will look on their own bodies before making a purchase.

This technology not only benefits people with disabilities who may struggle with dressing or shopping but also aids all consumers in making informed purchasing decisions.

At the heart of virtual try-on lies 3D human pose estimation, which synchronously rotates items to match various face angles or body shapes, enhancing the overall online shopping experience.

Make Use of Human Activity Recognition Models Today

Human activity recognition is a fascinating technology within computer vision, having diverse applications across industries. By utilizing machine learning techniques and sensors, HAR can identify and categorize human activities and movements. Its potential spans across various sectors, from healthcare to sports performance analysis, video surveillance, and beyond.

At Neurond AI, we craft tailored tools that interpret and extract insights from visual content. Leveraging the power of deep learning, our solutions streamline the process of data extraction from videos or images, significantly reducing manual efforts for your organization. With our top-notch artificial intelligence human activity recognition services, you can rest assured of the accuracy and reliability of our solutions.

For more information, contact us now!