SpatialCortex featured in IOSH Magazine: AI wearable sensors and Musculoskeletal Disorder prevention
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- 10 min read
SpatialCortex was recently featured in the latest issue of IOSH Magazine. We wish to share an extended version of the perspectives and insights presented in the feature. Please follow the link below to access the full article on pages 64–66.
The scale of work-related MSDs in the UK
The latest statistics from Great Britain's Health and Safety Executive (HSE), published in November 2025, show that more than half a million workers experienced new or ongoing work-related musculoskeletal disorders (MSDs) during 2024/25. These conditions resulted in over seven million lost working days, with the greatest impact seen in construction, transport and storage, and administrative and support services. The scale of this burden demonstrates that MSDs remain one of the most significant occupational health challenges across the UK workforce (HSE, 2025).
This challenge is not unique to Great Britain. The most recent U.S. Bureau of Labor Statistics data for 2023–2024 show that MSDs account for substantial numbers of both days away from work (DAFW) and days away, restricted, or transferred (DART) cases. Workers aged 25–54 experience the highest volume of DART cases, while women represent a significant proportion of this burden — 41% of DART cases and 37% of DAFW cases — highlighting the importance of considering demographic and task-related exposure when addressing MSD risk (BLS, 2024).
While established ergonomics assessments, training programmes, and risk controls have contributed to improvements over time, traditional approaches alone are increasingly insufficient to address today's MSD risk. These methods are often periodic and subjective, relying on assumed task design rather than continuous exposure. In practice, work is dynamic: tasks vary, workers adapt, and pressures such as fatigue, time constraints, and changing environments reshape how activities are performed. Without objective visibility into these real-world conditions, risk assessments can overlook cumulative strain, emerging behaviours, and early warning signs.
Taken together, these findings highlight a fundamental limitation of traditional risk management: it is often based on how work is intended to be done, rather than how it is actually performed. The gap between "work as planned" and "work as done" means MSD risks can remain hidden until injury occurs. This is where technology plays an important role — enabling organisations to understand true exposure, detect risk earlier, and implement controls where they will be most effective.
"Work as planned" versus "work as done": the gap wearable sensors can close
Founder and CEO Kailash Manohara Selvan explains the core problem in the IOSH Magazine feature:
"Our specialty is understanding the risks and why they happen. If we understand where the strain is building and the difference between work as planned versus work as done, then that will be more impactful." — Kailash Manohara Selvan, IOSH Magazine Q2 2026 (pages 64–66)
A manual handling risk assessment tells you how a task is supposed to be performed. It does not tell you how it is actually performed at hour six of a shift, under time pressure, by someone who has adapted their technique over years of doing the job. That gap is where musculoskeletal injuries develop — and it is invisible to any method relying on observation windows, spot checks, or self-reporting.
Wearable sensor data closes that gap. That is the principle behind MOVA.
The workplace MSD technology landscape
A range of technologies has been developed to help prevent musculoskeletal disorders, broadly falling into three categories: single-sensor devices, camera-based analysis systems, and full-body sensor configurations. Understanding the differences matters — not every solution provides an equally accurate picture of how work is actually performed.
Single-sensor solutions typically track spinal movement via a device clipped to the back or shoulder. They are simple to deploy and can be useful for basic movement awareness and coaching. The limitation is scope: by capturing only one part of the body, they miss other critical contributors to MSD risk — reaching distance, upper limb loading, leg posture — leaving significant exposure unmeasured.
Camera-based systems use video and AI to analyse worker movement. Because they rely entirely on AI interpretation of visual data, they can make incorrect assumptions about posture, load, or movement that a physics-based model would not. Assessments often require the evaluator to remain on-site, which can influence how workers move while being observed. Camera-based approaches also raise legitimate privacy concerns: participants' faces are typically captured and uploaded to the cloud for analysis, which creates real data protection risk and — critically — can undermine the staff acceptance that any successful programme depends on. For safety technology to work, workers must trust it. Approaches that feel like surveillance rarely earn that trust.
SpatialCortex takes a deliberately cautious approach to AI. The core of MOVA's risk assessment is built on physics-based mathematical models — controllable, explainable, and grounded in established biomechanical principles. AI is used selectively, for risk profile analysis and mitigation guidance, not as the primary engine for determining whether a movement is hazardous. This is a conscious design choice. As Kailash put it in IOSH Magazine: "We use it in a very selective way, and stakeholders have every right to understand how it is being applied." When workers and safety professionals can see how a risk score was derived, trust follows. When they cannot, resistance does.
Full-body sensor configurations, like MOVA MMH, use multiple sensors across the whole body to create a complete three-dimensional picture of movement and posture across a task. This approach captures what single-sensor and camera-based systems miss — and does so without video, without facial capture, and without requiring anyone to stand and observe.
How MOVA measures ergonomic risk
MOVA MMH is SpatialCortex's wearable biomechanical monitoring system for manual material handling environments. It uses nine body-worn sensors across the torso, arms and legs — capturing full-body, three-dimensional movement data across an entire working task or shift.
The core assessment engine is physics-based: controllable mathematical models grounded in biomechanical principles. The AI layer sits on top, analysing risk profiles and generating targeted mitigation recommendations aligned to the hierarchy of controls.
The output: automated MAC (Manual Handling Assessment Chart), RAPP (Risk Assessment of Pushing and Pulling), and ART (Assessment of Repetitive Tasks) scores — HSE-standard, reproducible, and generated without an assessor on the floor.
MOVA has been deployed across the rail industry, ports, manufacturing and warehousing — in some of the most physically demanding operational environments in the UK.
MOVA is also available in a MOVA SEAT configuration for roles involving prolonged seated operation, such as machinery operators and logistics gate personnel.
Delivering a successful safety initiative using safety technology
For sensor technology and AI to be accepted and truly successful in reducing work-related MSDs, several technical, organisational, and human conditions must be met. Advanced technology alone is not sufficient. Its effectiveness depends on how well it is embedded into safety management systems, workplace culture, and decision-making processes. The real value emerges when technology enables action, not just data collection.
When IOSH Magazine asked Kailash what it actually takes for wearable ergonomic technology to work in practice, he didn't lead with the hardware. He led with the organisation:
"First, there has to be an appetite across the organisation to use data to make things better. Data on its own doesn't reduce injuries. Second, this can't be standalone. It has to feed into the health and safety management system. Third, you need a genuine mindset of staff acceptance. You should also understand and be able to articulate where AI is being used. If people feel they don't have control over it, they will reject it." — Kailash Manohara Selvan, IOSH Magazine Q2 2026
Appetite. Integration. Acceptance. Miss any one and the technology — however capable — produces reports that change nothing.
1. Objective data that makes musculoskeletal risk visible
The technology must generate objective, reliable, and meaningful data that clearly exposes MSD risk — capturing task variability, repetition, awkward postures, load handling, cumulative exposure, and fatigue accumulation over time. When risks are made visible in a way that is intuitive and credible, they can be understood by both workers and decision-makers, increasing the likelihood of preventive action.
Trust in ergonomic technology is strengthened when outputs are based on sound biomechanical principles and aligned with recognised standards — HSE tools (MAC, RAPP, ART), ISO standards, or comparable international frameworks. Outputs must be transparent and explainable. There is no black box: a worker, a union representative, or a regulator can understand exactly how a risk score was calculated and what it means.
2. Appetite and leadership support to act on findings
Data alone does not reduce injuries. Insights from wearable ergonomic sensors must be translated into action — task redesign, process changes, training interventions, mechanical aids. This requires visible and sustained support from senior leadership: management must be willing not only to endorse the technology but to act on its findings by allocating resources and approving changes.
The example Kailash gives in the IOSH Magazine feature is a straightforward one: a manual handling task at a port warehouse where workers were handling goods from shoulder height to below knee level — a high-risk range for spinal loading. A small adjustment to working height reduced the risk immediately. The data made the problem visible. The fix was proportionate and fast.
3. A shared mission — not a siloed responsibility
Reducing MSDs is not a task that belongs to any single team. Health and safety professionals are already stretched, and placing the full weight of a technology programme on one department is a reliable way to see it stall. What drives successful implementation is a shared mission — a clear, organisation-wide commitment to reducing physical harm — that gives the programme momentum across health and safety, operations, HR, engineering, and frontline management simultaneously.
In practice, one team will naturally lead and coordinate. But the initiative succeeds when it is understood as everyone's concern: because the workers at risk sit across every department, and the changes required — task redesign, equipment decisions, scheduling, training — cut across all of them too. When the goal is framed as a mission rather than a project assignment, it travels further and sustains momentum longer.
4. Ongoing risk assessment — not permanent monitoring
It is important to be clear about what wearable ergonomic monitoring means in practice. Workers in a given role wear the sensors during their normal tasks for defined assessment periods — providing a representative picture of real physical exposure for that job. This is not all-day, every-day monitoring of every individual. It is structured, periodic data collection that builds a robust and ongoing understanding of risk across roles and environments over time.
This matters because MSDs develop through cumulative exposure over months and years. A single one-off assessment — however thorough — cannot capture how risk evolves as tasks change, workforces age, or interventions take effect. Repeating assessments at regular intervals, and tracking risk over time, is what enables organisations to move from reacting to injuries after the fact to identifying and addressing risk before harm develops. As Kailash puts it:
"At least a year of data, along with staff feedback, is needed after implementing a mitigation to show that there has been an improvement statistically." — Kailash Manohara Selvan, IOSH Magazine Q2 2026
5. Staff and stakeholder engagement from day one
Worker acceptance is the make-or-break factor for any wearable health technology programme. Employees engage with sensor-based systems when there is clear communication about what is being measured, why, and how the data will and will not be used. Strong guarantees around privacy, anonymisation, and data protection are essential — alongside a firm commitment that the technology is being used for prevention, not surveillance.
"We are not introducing a new process — we are just helping the process become more automated and data-led. But as a tech provider, we have to be transparent on where the AI comes in. We use it in a very selective way, and stakeholders have every right to understand how it is being applied." — Kailash Manohara Selvan, IOSH Magazine Q2 2026
When workers see tangible improvements resulting from the data, long-term engagement follows:
"We have had situations where people were critical to begin with, but then went on to become vocal advocates." — Kailash Manohara Selvan, IOSH Magazine Q2 2026
Conclusion
The people who load, unload, moor, drive and handle goods across the UK's ports, warehouses and logistics networks are doing physically demanding work every shift. The tools most organisations currently use to manage that risk were not built for the reality of how that work is actually done.
That gap — between work as planned and work as done — is where MSDs develop. Closing it requires objective data, the organisational will to act on it, and the trust of the workers it is meant to protect. None of those three things is easy. But when all three come together, the trajectory changes: from reacting to injuries after the fact, to preventing them before they occur. That is what our work is for.
Read the IOSH Magazine feature and explore MOVA
"Sensor sensibility" — the full IOSH Magazine article featuring Kailash Manohara Selvan — appears on pages 64–66 of the Q2 2026 issue, written by John Windell.
→ Full magazine: Q2 2026 | IOSH magazine
Frequently Asked Questions
Can AI wearable sensors actually prevent musculoskeletal disorders?
AI wearable sensors can significantly reduce MSD risk when implemented correctly. They provide objective visibility into how work is actually performed — capturing cumulative exposure, awkward postures, and fatigue accumulation that periodic manual assessments miss. However, technology alone is not sufficient: reducing MSDs requires organisational appetite to act on findings, integration with the health and safety management system, and genuine worker engagement.
What is the difference between MAC, RAPP and ART assessments?
MAC (Manual Handling Assessment Chart), RAPP (Risk Assessment of Pushing and Pulling), and ART (Assessment of Repetitive Tasks) are HSE-standard ergonomic risk assessment tools used in the UK. MAC is used for lifting, lowering and carrying tasks; RAPP covers pushing and pulling; ART assesses repetitive upper limb tasks. MOVA MMH generates all three automatically from wearable sensor data, without requiring manual observation or a trained assessor on-site.
What does "work as planned versus work as done" mean in ergonomics?
"Work as planned" refers to how a task is designed and intended to be performed. "Work as done" is how it is actually performed in real conditions — accounting for fatigue, time pressure, task variability, and individual adaptation. The gap between the two is where musculoskeletal risk is often concentrated and where traditional risk assessments fail to capture true exposure.
How many sensors does MOVA MMH use?
MOVA MMH uses nine body-worn sensors attached to the torso, arms and legs, enabling full-body three-dimensional motion capture across a working task or shift. This multi-sensor approach distinguishes it from single-sensor solutions that track only spinal movement and miss critical contributing factors such as reaching distance and upper limb loading.
Does wearable ergonomic monitoring mean workers are tracked all day?
No. Workers in a given role wear the sensors during defined assessment periods — providing a representative picture of real physical exposure for that job. This is structured, periodic data collection, not permanent individual monitoring. The goal is to build an accurate, ongoing understanding of risk across roles and environments so that organisations can intervene before harm develops.
How long does it take to see results from ergonomic risk monitoring?
Immediate risk visibility is possible — organisations can identify high-risk tasks and implement targeted changes within weeks. However, demonstrating statistically significant reductions in MSD rates requires at least a year of data, combined with staff feedback, to account for the range of variables that influence absence and injury figures.
