ErgoAssist is presented, a head-worn ergonomic assistant that detects poor posture using IMU-based head tracking and estimates task-induced cognitive load using a consumer-grade EEG headband for continuous everyday use.
Abstract
Prolonged digital device use has made poor posture and musculoskeletal discomfort pervasive among knowl- edge workers. Existing ergonomic wearables rely solely on posture thresholds, frequently interrupting users during high-focus moments and leading to alert fatigue and abandonment. Yet posture and cognitive load are closely coupled, and most systems remain cognitively unaware. We present ErgoAssist, a head-worn ergonomic assistant that detects poor posture using IMU-based head tracking and estimates task-induced cognitive load using a consumer-grade EEG headband for continuous everyday use. In a controlled lab study, ErgoAssist achieves 81% posture classification and 90.2% task induced cognitive load estimation accuracy under leave-one-subject-out evaluation. In a preliminary real-time deployment, cognition-aware alerting reduces alert frequency by 81%, improves perceived usability by 43%, task performance by 25%, and improves posture correction rate by 38%, delivering fewer but better-timed interventions rather than merely suppressing alerts.
Prolonged sitting in academic and office settings is strongly linked to occupational musculoskeletal disorders, including lower back pain, neck tension, shoulder stiffness, and spinal strain. Traditional ergonomic interventions often fail because they rely on user self-regulation, which diminishes during periods of hig...
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Prolonged sedentary behavior in modern workplaces is a primary catalyst for musculoskeletal disorders (MSDs). Existing ergonomic chairs provide only passive support, while wearable posture trackers are intrusive and non-scalable for daily use. This paper presents an intelligent ergonomic chair: a noninvasive, active In...
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This preliminary laboratory validation suggests that the Ergo-PAS can provide reasonably accurate and repeatable posture measurements using smartphone-based inertial sensors and shows promise as a low-cost platform for real-time ergonomic risk monitoring and providing feedback.
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Musculoskeletal disorders and posture-related conditions are increasingly recognized as major public health concerns in modern sedentary societies. Belt over-tightening is a common but rarely monitored daily habit that has been associated with increased abdominal compression, altered pelvic mechanics, and posture-relat...
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Wearable sensors are crucial in monitoring physiological responses to mental workload (MWL), offering real-time insights into cognitive and emotional states. Accurate assessment of MWL is essential in fields like aviation, automotive industries, and high-stress work environments where cognitive overload can lead to dec...
Despite advancements in automating observation-based ergonomics assessments, most automations still rely on a single underlying construct (e.g. posture). This fragmented approach, however, derogates from the integrated nature of traditional assessment methods. The few studies that consider multiple aspects do so throug...
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Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduSep 30, 2026
Able to defeat top-ranked human players and more efficient than other models, the new system could help decision-makers in military maneuvers or business negotiations.
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