Israeli Scientists Discover Gait Can Reveal Hidden Health Conditions
Translated & summarized from Cursorinfo by baba
Israeli scientists have discovered that human gait can reveal hidden health conditions, including liver elasticity, metabolic issues, and depression, using AI analysis of body movements. The study, involving over 3,300 volunteers and published in Nature Medicine, found that AI could identify significant health correlations from video recordings of participants' movements. This technology could potentially be used for non-invasive health assessments in clinical settings, offering a new way to monitor patient well-being without special sensors.
The story in 6 lines · by baba
- Israeli scientists found gait analysis can reveal hidden health conditions using AI.
- The study involved 3,328 volunteers and was published in Nature Medicine.
- AI linked body movements to liver elasticity, metabolism, and depression symptoms.
- Gait analysis proved accurate for assessing BMI and visceral fat.
- The technology could enable non-invasive health monitoring in clinical settings.
- Leg movements relate to metabolism and strength, torso movements to sleep and lifestyle.
Israeli researchers have found that a person's gait, including the movements of their legs and torso, can reveal information about various bodily systems, often without the need for advanced technology. A study led by Professor Eran Segal at the Weizmann Institute in Rehovot, published in Nature Medicine, utilized artificial intelligence to link subtle body movements to a range of health indicators. The research involved 3,328 adult volunteers from the Israeli Human Phenotype Project, who performed five movement exercises captured by a 3D motion-tracking camera. AI analyzed the position of 26 joints, gait speed, stride length, limb movements, and coordination patterns from 17,589 video recordings.
By comparing these movement data with participants' medical records, including blood tests and cardiovascular health, the AI identified 1,887 statistically significant connections among 3,210 studied parameters. The technology proved particularly accurate in assessing body mass index and visceral fat. More surprisingly, gait analysis provided insights into liver elasticity, a key indicator for fibrosis, even after accounting for age and weight, sometimes matching the significance of biochemical tests.
The study also uncovered unexpected links to mental health, with specific movement patterns correlating with depressive states and improving the identification of individuals reporting depression. Connections were also found with the use of antidepressants, antihistamines, and stomach acid reducers. Researchers emphasize that gait alone cannot diagnose specific conditions or medications but offers supplementary information.
Further analysis revealed that leg movements are more indicative of metabolism, strength, and frailty, while torso movements better reflect sleep patterns and lifestyle. These correlations persisted even after controlling for age, height, and weight, suggesting the technology can glean health information beyond basic physical characteristics. The team envisions future applications in hospitals for preliminary health assessments via simple video recordings, offering a non-invasive method to monitor patients and treatment outcomes without requiring special sensors.
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