How can video analytics enhance human activity recognition in healthcare and assistive technologies?

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Video analytics is the process of extracting meaningful information from digital video data, such as objects, faces, actions, events, and scenes. It can be used for various purposes, such as security, surveillance, entertainment, education, and healthcare. In this article, we will focus on how video analytics can enhance human activity recognition (HAR) in healthcare and assistive technologies. HAR is the task of identifying and classifying human actions and behaviors from video data, such as walking, sitting, falling, eating, or sleeping. HAR can have many applications in healthcare and assistive technologies, such as monitoring the well-being and safety of elderly or disabled people, detecting abnormal or risky situations, providing feedback and guidance, and supporting diagnosis and treatment.

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Victoria Matytsyna

Head of Business Development at DSSL | Video Analytics & Surveillance Solutions

1 Benefits of video analytics for HAR

Video analytics can offer a plethora of advantages for HAR in healthcare and assistive technologies. Such as, its ability to leverage advanced techniques such as deep learning, computer vision, and natural language processing to achieve high accuracy and reliability in HAR. Moreover, video analytics can handle complex and diverse scenarios, from different lighting conditions to camera angles and occlusions. It can also capture rich and multimodal data from video streams, such as visual, audio, and textual information. This data provides more comprehensive and contextual insights into human activities and behaviors. Additionally, video analytics is highly scalable and flexible, able to handle large volumes of video data from multiple sources and locations while adapting to different user needs and preferences regarding privacy, security, and personalization.

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Victoria Matytsyna

Head of Business Development at DSSL | Video Analytics & Surveillance Solutions
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Based on my experience, video analytics offers significant benefits for Human Activity Recognition (HAR) in healthcare and assistive technologies. By using deep learning and computer vision, it achieves high accuracy even in complex conditions like varying lighting or camera angles. Its ability to process multimodal data—visual, audio, and textual—provides a more comprehensive understanding of human behavior. Additionally, video analytics is scalable, handling large volumes of data from multiple sources while addressing privacy and security concerns, making it a versatile tool for improving patient care and support.

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Kunal Dargan

MS(Research) at IIT Delhi | Computer Vision | Surgical Datascience | Medical AI
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In my experience, human activity recognition (HAR) can play a central role in estimating outcomes of clinical procedures on patients, in Surgical OR (operation rooms) management, and in analyzing psychological patterns, apart from many other applications. Real-time monitoring systems enabled with automated analysis through state-of-the-art Video analytics models can alert operators and healthcare providers about abnormal behaviour. These models can use the vast quantity of video data in storage devices to make predictions. Since data is spatiotemporal (space and time) and is available in image frames and changes over time, models built with such data often resist small changes and give confident and consistent predictions.

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2 Challenges of video analytics for HAR

Despite the benefits, video analytics also faces some challenges for HAR in healthcare and assistive technologies. Ethically, it raises issues such as privacy, consent, trust, accountability, and bias. Practically speaking, it may require high-quality and diverse data to train and validate the models and algorithms for HAR. Additionally, it may depend on the availability and interoperability of video sources and devices like cameras, sensors, and networks. Finally, video analytics may encounter errors or limitations in the performance and robustness of the models and algorithms for HAR.

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Kunal Dargan

MS(Research) at IIT Delhi | Computer Vision | Surgical Datascience | Medical AI
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Ensuring privacy preservation is crucial when gathering data, particularly from CCTV footage, necessitating explicit consent from individuals. However, many companies and data collectors often fall short in maintaining vigilance, leading to scrutiny. The redundancy problem with videos poses a significant bottleneck. Models frequently learn from repeated or similar-looking frames, which can account for 90% of their learning process. This creates data imbalance issues, leading to low recall. Scalability and processing large volumes of video data in real time can be computationally demanding, presenting challenges. Efficient algorithms and hardware acceleration techniques are essential to enable real-time HAR in large-scale deployments.

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3 Best practices for video analytics for HAR

To ensure the successful implementation of video analytics for HAR in healthcare and assistive technologies, some best practices should be followed. These include user-centered design, which involves respecting the user's needs, preferences, and rights, as well as involving the user's participation and consent in the data collection and processing process. Additionally, ethical and legal compliance should be ensured, with particular attention to privacy, security, and transparency of the data and models. Lastly, evaluation and improvement should be conducted regularly to guarantee the quality, accuracy, and reliability of the HAR. This should include testing and validating in real-world scenarios and contexts, as well as updating and refining based on feedback and results.

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4 Examples of video analytics for HAR

To illustrate the potential and impact of video analytics for HAR in healthcare and assistive technologies, here are some examples of existing or emerging projects and applications. CareView is a video-based system that monitors activities and behaviors of hospital patients to prevent falls and injuries. Kinetikos is a platform that uses computer vision and biomechanics to measure and quantify the mobility and function of patients with musculoskeletal disorders. PARO is a video-based system that recognizes emotions and behaviors of elderly people with dementia or cognitive impairment, using computer vision and natural language processing. It also utilizes a robotic seal as an interactive and therapeutic device to provide comfort, stimulation, and companionship.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can video analytics enhance human activity recognition in healthcare and assistive technologies?

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1 Benefits of video analytics for HAR

2 Challenges of video analytics for HAR

3 Best practices for video analytics for HAR

4 Examples of video analytics for HAR

5 Here’s what else to consider

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