Motion recognition to improve public health

Motion recognition to improve public health

This post is a quick overview of an Abto Software blog article.

In today’s fast-changing world, heavily influenced by ever-evolving computational technology and digitization, the ability to leverage the potential of cutting-edge movement recognition is transforming not only daily living but also collective well-being.

By implementing artificial intelligence, motion recognition swiftly becomes a force with the promising potential to revolutionize public health, especially epidemiology, disease diversion, emergency preparedness, and more. Quickly assessing and analyzing movement patterns, motion recognition can facilitate healthcare delivery, prevent disease, prolong life, and create happier and healthier communities.

What is motion recognition?

Motion recognition, a technique, is the automatic distinction and processing of complex movement patterns. The technology is being actively utilized to detect, accurately analyze, and respond to patterns being detected, and that across industries.

Movement recognition has already gained popularity among forward-looking business leaders across domains. It is being integrated in the healthcare industry, in finance and retail, education, government, and others.

Motion recognition’ key components

Motion recognition’ key components, by Abto Software

Hardware Components

  1. Data collection: smartphone and tablet cameras, accelerometers, gyroscopes, infrared, ultrasonic, radar sensors

  2. Data processing — high-speed processors handling vast data amounts in real-time, enabling automatic information input from sensors

  3. Data storage — secure warehouses temporarily preserving data collected, enabling seamless information access and analysis

Software Components

  • Computer vision — CV algorithms process visual data captured from cameras to detect and analyze motion patterns, uncover changes between frames, and even track objects and individuals

  • Machine & deep learning — ML and DL algorithms recognize specific data types to identify and interpret different movements or gestures, enhancing accuracy

  • Signal processing — these algorithms process non-visual information captured from sensors to assess motion patterns

  • User interfaces — this component allows users to interact with systems, set preferences, get feedback, and more

  • Data filtering — these techniques help refine and predict data captured to account for inaccuracies

  • Data fusion — these combine data from multiple sensors to get a more comprehensive understanding of movements and gestures

Practical application

Movement recognition has witnessed significant advancements, revolutionizing operations across industries. From different healthcare verticals to retail, education, government, construction, logistics, and others, movement assessment and analysis has found practical applications accelerating safety, workflow efficiency, and other critical aspects.

The technology is currently automating processes all across:

  • Finance (fraud detection and prevention, customer satisfaction and retention)

  • Retail (smart conveyors, automated checkout)

  • Education (attendance monitoring, learning gamification)

  • Government (smart city, public service)

  • Construction (safety monitoring, equipment control, VR and AR training)

  • Transportation & logistics (security checks, warehouse operations, traffic management)

  • Automotive — motion detection and analysis can be used for driver monitoring and assistance, and advanced gesture control for navigation, and more

  • Agriculture — motion recognition might optimize machinery and drone operations by leveraging gesture-based control

  • Entertainment and gaming (interactive games, art installations)

  • Tourism and leisure (movement monitoring managing crowds, gesture-based interfaces for directions and information)

Healthcare applications

Movement recognition considerably influenced conventional approaches across different healthcare verticals. From optimizing physical therapy and rehabilitation to supporting surgical professionals and facilitating cardiology and radiology outcomes, motion analysis is showing great potential.

Let’s delve into some of the healthcare applications of the innovative technology:

  • Physical therapy and rehabilitation — pose detection to empower patients during post-surgical recovery

  • Surgical training and assistance — pose estimation to support specialists performing surgical procedures

  • Elderly care (fall detection, gait analysis, exercise guidance, and assistance in basic daily activities)

  • Sports medicine (risk assessment, injury prevention and treatment)

  • Orthopedics — motion recognition to diagnose and treat musculoskeletal conditions

  • Pediatrics — movement recognition to enable early assessment and treatment of developmental movement disorders

  • Neurology — studying disorders such as Parkinson’s disease, essential tremor, and others

  • Pulmonology — studying how respiratory health is affected by movement and exercise

  • Cardiology — activity monitoring to improve heart health

  • Radiology — movement assessment to enhance image processing

Why apply motion recognition to improve public health?

Motion recognition is an innovative technology that promises great opportunities to transform public health. Unlike other modern-day technologies, movement assessment and analysis might enable instant monitoring, real-time feedback, gesture-based interfaces, interaction analysis, and more dynamic adoption that similar advanced solutions.

By integrating motion recognition, responsible decision-makers might transform:

  • Data collection and processing — data collection, when automated, might provide better understanding of metrics such as personal habits, health conditions, and adherence

  • Real-time monitoring and surveillance — real-time monitoring might enable faster responses to issues associated with public health

  • Patient satisfaction and loyalty — the algorithm can make the interaction between patients and today’s healthcare systems more intuitive and convenient, naturally improving healthcare accessibility

  • Resource allocation — the technology can provide valuable insights into areas requiring focus, eventually enhancing informed decisions associated with process optimization

  • Workflow automation — repeated routines (for example health tracking) can be seamlessly automated, this way enabling professionals to prioritize patient-first care

  • Intervention customization — specialized treatments, for example physical therapy and rehabilitation, can be better personalized, thus boosting customer engagement

  • Prevention strategies — by gathering valuable insights from motion recognition data, local authorities can develop more targeted prevention strategies

  • Environmental design — by understanding people’s interactions within environments, local authorities can make tailored modifications to facilities and other public spaces