For a more detailed view, patients need to visit a motion analysis lab, where hourslong biomechanical assessments require highly trained technicians and equipment that costs hundreds of thousands of dollars. \u201cThe status quo is that very few people can have their motion measured, and this is rarely used clinically \u2014 usually between zero and once in a person\u2019s lifetime,\u201d Delp said.<\/p>\n
To test whether mobile phones could do the job, Delp and his collaborators used up to three smartphone cameras to record nearly 130 people as they performed nine movements, such as a 10-meter run and calf raise. Two-thirds of participants had a neuromuscular disease \u2014 facioscapulohumeral muscular dystrophy (FSHD) or myotonic dystrophy (DM) \u2014 while the rest had no diagnosed movement problems. At the same time, clinical evaluators performed four traditional timed function tests. The process took an average of just 16 minutes.<\/p>\n
Researchers converted the videos into 3D models using OpenCap<\/a>, an open-source tool that Delp and his team at Stanford released in 2023. The software automatically created a \u201cdigital twin\u201d of each participant, allowing the team to measure range of motion, stride length, speed and other aspects of movement. Researchers then translated the data into 34 features of movement that are relevant to FSHD and DM, such as how high patients lift their ankles while walking.<\/p>\n Based on the smartphone data, researchers inferred nearly identical time scores to those measured with a stopwatch. When a subset of participants repeated the tests the next day, the smartphone system proved just as reliable. \u201cWith just a video, you can reproduce what an experienced and busy clinician would do in a clinic,\u201d Delp said. A better diagnostic tool<\/p>\n The videos also revealed disease-specific movement patterns that timed tests can\u2019t capture. For example, people with FSHD took shorter strides and lifted their ankles higher while walking, while those with DM had more difficulty rising from a chair. Based on the footage, a computer model could identify the disease a person had with 82% accuracy, compared with 50% accuracy for the stopwatch method.<\/p>\n The findings suggest that analyses once confined to specialized labs can now be done quickly, anywhere and for free.<\/p>\n \u201cIt\u2019s really encouraging,\u201d Delp said. \u201cBy democratizing access with smartphone videos, we think we\u2019ll be able to detect movement disorders for free in the community. We can detect diseases earlier so patients can seek treatment sooner or participate in drug trials earlier.\u201d<\/p>\n Delp and his team have begun examining how tools like OpenCap can be incorporated into clinical trials. His hope is that this approach will make measurements of therapies for neuromuscular diseases more precise, accessible and easy to implement. \u201cWe\u2019ll have more sophisticated measures to see if therapies are working,\u201d he said.<\/p>\n In the meantime, thousands of labs around the world are already using OpenCap to assess conditions such as cerebral palsy and arthritis. Germany\u2019s national volleyball team, for example, used the tool to evaluate sports injuries in 160 athletes. \u201cIt used to take them years to get that kind of data, and with OpenCap they did it in one season,\u201d Delp said. \u201cThey\u2019re gaining insight into how they can perform better, avoid injury and improve faster.\u201d<\/p>\n Delp emphasizes that further research is needed to ensure the tool\u2019s accuracy for each new application. Still, he believes the technology represents the future of how doctors diagnose and track movement disorders. \u201cThis method of accurately and rapidly assessing movement is on the verge of transforming multiple fields,\u201d he said.<\/p>\n Scott Uhlrich, who earned a PhD at Stanford University and is now assistant professor at the University of Utah, is also a first author on the study. Stanford Medicine\u2019s John Day<\/a>, MD, PhD, professor of neurology, and research scientist Tina Duong<\/a>, PhD, and their team also played a major role in the study.<\/p>\n Funding came from the Wu Tsai Human Performance Alliance; the Mobilize Center at Stanford University; and the Myotonic Dystrophy Foundation, which played no role in the study design or analysis.<\/p>\n