Ai-Move: the artificial intelligence platform that detects physical health-related conditions in the workplace

Ai-Move is a unique digital service aimed at health prevention and motivation in the workplace that measures and analyses physical movement to help with the early detection of problems.

Ai-Move is a unique digital service aimed at health prevention and motivation in the workplace that measures and analyses physical movement to help with the early detection of problems.

Nearly one third of EU workers and just under half of those aged over 55 have suffered from musculoskeletal disorder pain for at least a week in the past month.*

Ai-Move is a unique digital service aimed at health prevention and motivation in the workplace that measures and analyses physical movement to help with the early detection of problems. The service benefits both workers (optimising health) and employers (boosting productivity and reducing absentee costs).

Ai-Move is funded by EIT Digital's Digital Wellbeing Action Line for 2018, with RISE SICS as activity lead.

* EUMSUC (2008-2013). Musculoskeletal Health in Europe Report v5.0. EUMSUC, Executive Agency for Health and Consumers. EUMUSC.Net is an information and surveillance network of 22 institutions across 17 countries, supported by the European Community (EC Community Action in the Field of Health 2008-2013) and EULAR.

Musculoskeletal disorders (MSDs) remain one of the major causes of work-related illness and long-term sickness in the European Union across all sectors and occupations, especially among aging populations. Besides the effect on workers themselves, MSDs can lead to higher costs to business and society as a whole' 1.

MSDs cover a broad range of health problems. The main groups are back pain/injuries and work-related upper limb disorders, commonly known as "repetitive strain injuries" (RSI).

AI-Move

Download Ai-Move poster

Unique and impactful solution

Supported by EIT Digital and in the Digital Wellbeing Action Line portfolio, the Ai-Move project integrates Qinematic's Posture Scan, an optical sensor service that gathers detailed information about movement patterns. Ai-Move analyses all the scan data and other metadata to reveal existing or potential problems, often before symptoms arise. The data is visualised through a secure web app available to both the employee and the health service provider. Over time, individuals can see their own progress and compare it with population-based data.

Marie Sjölinder, Activity Lead of Ai-Move explains: "Modern and personal motivating health services like Ai-Move can make employers more attractive, improve staff retention, increase productivity and reduce the risk and the cost, of sickness absence and job dissatisfaction."

The results are used by health providers to personalise recommended treatment plans, such as specific exercises, and individuals are also offered motivational support. Through scientifically-validated assessments and 3D visualisation, the service improves awareness and empowers people to be proactive about maintaining optimal health and/or compliance with rehabilitation plans.

Affected employees respond much better to early and individualised interventions, and will enjoy a better quality of life, without the torment of physical pain or dysfunction at work.

Competitive Advantage:

  • Proprietary skeletal tracking algorithms for accurate data acquisition
  • Automated scan process for convenience and ease of use
  • Decisions-support algorithms based on the analysis of thousands of scans
  • Cloud based for scalability and remote service delivery
  • Advanced knowledge about movement science and the health and wellness industry

Markets

  • Target markets: health service providers and insurers active in occupational health in Europe, Australasia and Africa.
  • Target groups: employees in risk-related jobs to increase health awareness and prevention.

Consortium partners

Ai-Move will be introduced to the occupational health market by the scaleup company Qinematic as an additional service to automated decision support and digital communication between employees and health service providers. Qinematic already provides an existing service for Dynamic Posture Scanning for Health and Fitness and has customers in the Australia, Belgium, Germany, Holland and the Nordics, including successful pilots with insurance companies delivering the service to the workplace. Qinematic has Hannover Re as a commercial partner and access to international markets.

RISE SICS (Swedish research institute)) is the Activity Lead on the Ai-Move project and contributes to project management, machine learning and user-motivation methods;

Bright Cape (Data science & analytics expert company in NL) is developing the machine learning platform and the visualisation web application in the Ai-Move project.

Oulu University (Interact research group in Finland focusing on human-computer interaction) contributes to user aspects including user motivation.

Roadmap

Throughout 2018, the consortium will develop and validate a machine learning platform that analyses Qinematic 3D scan data, and a web application that visualises results for health service providers and employees. Qinematic will develop the business for the Ai-Move service and will introduce Ai-Move into the occupational health market in early 2019. Further validation and verification of new approaches to analysis and visualisation of data will be conducted in 2019. Decision support functionalities will be expanded after that.

The Digital Wellbeing Action Line leverages digital technologies to help people stay healthy (prevention and early detection) or cope with an existing chronic condition. It includes both physical and mental wellbeing. The solutions generally rely on enabling consumers to be well-informed about their wellbeing, change their behavior and use digital unobtrusive instrumentation to monitor and improve their quality of life, saving on high healthcare costs later in life.


1 Woof, A.D. & Pfleger, B. (2003). Burden of major musculoskeletal conditions. Bulletin of the World Health Organization 2003, 81 (9).

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