The Science of Ergonomic
Injury Prevention

Soter devices are designed with the latest technology and combine unprecedented up-to-date effective training practises drawn from ergonomics, sports science & physiological science

SoterCoach Was Built Off Existing Standards


Our SoterCoach wearable device leverages the vast amounts of research that have quantified ergonomic injury risk including International Standards 11228-1 to 11228-6 & 12295, OSHA’s Ergonomics Program Standard 29 CFR 1910.900, Safe Work Australia’s Hazardous Manual Tasks Code of Practice, and more than a dozen peer-reviewed research projects.

SoterCoach utilizes an inertial motion sensor & world-leading data algorithms identify when a worker bends, twists, or puts a force on their musculoskeletal system, quantifying the movement to analyse if it’s a higher-risk movement that will cause risk to their musculoskeletal system.


SoterCoach Measures More Movements Than Any Other Product Developed

In total
  1. Lifting movements with poor technique (primarily sagittal flexion)
  2. Overreaching (sagittal flexion)
  3. Twisting (rotation of the trunk)
  4. Repetitive movements & forces
  5. Sustained awkward static postures (including sagittal flexion, sagittal extension, rotation of the trunk & lateral flexion)
  6. Sustained & high forces
  7. Sudden impact forces
  8. Full-body vibrations

And Now The System Is Starting To Overtake Traditional Standards, Powered By AI

Traditional standards specify what 'typical' humans can withstand. In the case of ergonomic and manual handling risk, these usually estimate how many higher-risk movements a person should make within a time period and how much weight or force a person should be able to safely withstand.

At Soter Analytics we've built a system that self-learns and gives personalised feedback to each individual worker, helping them avoid the movements that might increase their own risk of injury.

  1. Measures the individual strength of each worker with our highly-innovative intensity model which identifies how difficult each movement is.
  2. Identifies when an individual or group of movements are increasing the fatigue of a worker or if they begin overcompensating with different working techniques to avoid particular pain or lactic acid buildup.
  3. System continually self-learns to calculate how the worker's risk is adjusting and provides increasingly personalised feedback.

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