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Researchers are combining in-home sensor technology with artificial intelligence to monitor daily changes in ALS patients\u2019 health, paving the way for earlier interventions<\/a> and better quality of life.<\/p>\n Bill Janes is on a mission to improve life for people with amyotrophic lateral sclerosis (ALS). As a licensed occupational therapist and researcher at the University of Missouri, he\u2019s seen firsthand how the disease can steal a person\u2019s strength, speech, and independence.<\/p>\n ALS damages the nerve cells that control muscle movement, causing weakness and trouble with speaking, swallowing, and breathing. But the disease doesn\u2019t look the same for everyone. Some people decline quickly, while others lose function gradually.<\/p>\n To help close those gaps in care, Janes is working with experts at Mizzou\u2019s School of Medicine and Institute for Data Science and Informatics to build a smarter way to track ALS progression in real time. Their solution uses a combination of in-home sensors and artificial intelligence.<\/p>\n \u201cRight now, we\u2019re essentially blind to what\u2019s happening between clinic visits,\u201d Janes, an assistant professor in Mizzou\u2019s College of Health Sciences, says. \u201cWith these sensors, we can detect subtle shifts in health sooner\u2014sometimes even before a patient feels them\u2014and act before a crisis occurs.\u201d<\/p>\n Professor Emerita Marjorie Skubic at Mizzou\u2019s College of Engineering and Professor Emerita Marilyn Rantz at Mizzou\u2019s Sinclair School of Nursing originally developed the sensors to monitor the health of older adults living at home. The devices can detect changes in behavior and physical activity, including walking and sleeping patterns, prompting health care interventions that can delay or prevent serious health events.<\/p>\n Now, Janes and colleagues are adapting the sensors to fit the needs of ALS patients, whose functional decline often mirrors that of older adults but progresses more rapidly and unpredictably.<\/p>\n Right now, the team is focused on verifying that the sensor data accurately reflects real-world changes in how patients function day to day. Their next phase will make sense of the collected data using predictive modeling.<\/p>\n The data flows wirelessly through two small boxes in the home, then securely transfers to university systems, where researchers can study the results. Using machine learning, a type of AI, predictive models are built to estimate each patient\u2019s score on the ALS Functional Rating Scale Revised (ALSFRS-R)\u2014a clinical tool that measures how ALS affects a person\u2019s daily abilities over time, including walking, talking, swallowing and breathing.<\/p>\n Leading the project\u2019s data science efforts is Noah Marchal, a research analyst in the School of Medicine and a PhD candidate in health informatics at Mizzou\u2019s Institute for Data Science and Informatics.<\/p>\n \u201cOur goal is to not just track changes after they happen; we\u2019re also trying to see them in advance,\u201d Marchal says. \u201cFor example, we want to be able to detect a problem in gait or respiration before it causes a fall or hospitalization.\u201d<\/p>\n When Janes recognized how the sensors could transform ALS care, Marchal helped bring that vision to life with guidance from his advisor, Xing Song, an assistant professor of biomedical informatics in the School of Medicine.<\/p>\n For the final stage of the project, researchers will integrate the system directly into clinical workflows. If the model predicts a concerning decline, a clinician could receive an alert to check in with the patient, adjust medication, recommend assistive devices, or suggest further treatment.<\/p>\n Early feedback from participating families has been positive as many appreciate the sense of connection and peace of mind the system provides.<\/p>\n \u201cOur vision is that one day clinicians will have a secure portal where they can view a patient\u2019s daily health trends the way ICU teams monitor telemetry,\u201d Janes says.<\/p>\n \u201cIt\u2019s about giving people living with ALS\u2014and their care teams\u2014the information they need, when they need it.\u201d<\/p>\n While the current project focuses on ALS, this same technology could be adapted to help monitor other chronic conditions, such as Parkinson\u2019s disease or heart failure.<\/p>\n The study appears in the journal Frontiers in Digital Health<\/a>.<\/p>\n