This wearable technology integrates sensors into a back brace and shoe inserts to monitor posture and movement. Using AI-driven analysis, it provides real-time feedback via a software application, helping Direct Care Workers prevent the risk of back injuries during lifting and transfer tasks.
Background: Workplace injuries, particularly musculoskeletal disorders, are a major concern in direct care, where workers are routinely required to lift, transfer, and assist individuals with disabilities or limited mobility. Direct Care Workers (DCWs) in home and long-term care settings face a disproportionately high risk of back injuries due to the physical demands of their roles, often performed in tight spaces and unpredictable conditions. These injuries have serious consequences for DCWs’ health and well-being, and create substantial financial burdens for employers, insurers, and the healthcare system, with billions spent annually on treatment and workers’ compensation. As the need for caregiving services grows, effective and proactive solutions that reduce injury risk and improve workplace safety are urgently needed.
Current approaches to preventing back injuries among DCWs primarily rely on traditional training programs and generic wearable technologies. However, these methods have notable limitations. Training alone rarely leads to lasting behavioral change or adequately simulates the complex, real-world scenarios encountered by DCWs, resulting in a gap between knowledge and practice. Existing wearables, such as basic posture monitors and pressure sensors, are typically designed for static work environments or general material handling in industrial settings, lacking the precision, adaptability, and contextual awareness required for the dynamic and variable tasks of direct care. These devices do not provide real-time, actionable feedback tailored to the unique ergonomic challenges of patient lifting and transferring, nor do they integrate multiple risk factors into a unified system. As a result, DCWs remain vulnerable to injury, and employers struggle to implement interventions that effectively address the root causes of workplace harm.
Technology Overview: This technology is an integrated, AI-powered wearable system designed to prevent back injuries among Direct Care Workers (DCWs) in home care and long-term care settings. The system combines a sensor-enabled back brace and shoe inserts with an intelligent software application.
The back brace continuously monitors posture and identifies high-risk movements such as excessive back bending and spine twisting. The shoe inserts incorporate pressure and inertia sensors to measure foot pressure distribution, body orientation, squatting, lifting loads, and balance. Machine learning algorithms analyze the real-time data from these sensors to detect unsafe movement patterns and provide immediate corrective feedback through haptic or auditory signals. The companion software application serves as the system’s user interface, offering personalized feedback, ergonomic guidance, educational resources on safe lifting and transfer techniques, and tools for goal setting and progress tracking.
What sets this technology apart is its comprehensive and work-centered approach. Unlike generic wearable devices that focus on limited aspects of posture or movement, this solution integrates both back and foot data to capture the full range of biomechanical risks associated with patient care, especially in tight or unpredictable and complex care environments. The use of real-time machine learning analysis enables immediate, actionable feedback that supports continuous behavioral improvement beyond what traditional periodic training sessions can achieve. Additionally, the software’s personalized feedback and educational content ensure that users receive ongoing, context-specific support. This comprehensive, adaptive system addresses the shortcomings of existing technologies and training programs, offering a proactive and effective means of reducing workplace injuries and promoting the health and retention of DCWs.
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Advantages: • Reduces the risk of back injuries among Direct Care Workers through real-time monitoring and corrective feedback. • Integrates wearable sensors on back and feet to precisely detect high-risk movements, posture deviations, and lifting mechanics. • Uses machine learning algorithms to analyze movement patterns and provide immediate haptic or auditory alerts when unsafe behaviors occur. • Offers personalized ergonomic advice, educational content, and progress tracking via a user-friendly software application. • Enhances the effectiveness of on-the-job training by delivering continuous, context-specific feedback during actual care tasks. • Designed specifically for the unique biomechanical and ergonomic challenges faced by Direct Care Workers in home and long-term care settings. • Helps reduce healthcare, injury-related downtime, and workers' compensation costs by preventing workplace injuries. • Promotes worker safety, job satisfaction, and retention, ultimately improving continuity and quality of care for individuals with disabilities or limited mobility.
Applications: • Back injury prevention for Direct Care Workers • Real-time ergonomic training and skill reinforcement platform • Safety compliance monitoring for healthcare providers and caregiving organizations • Risk assessment and early identification of injury-prone movement for Worker compensation and workplace safety programs
Intellectual Property Summary: Patent application filed; 63/867,367 on 08/25/2026
Stage of Development: Prototype development TRL 2
Licensing Status: This technology is available for licensing.