Monitoring gait with wearable sensors holds promise for improving medical assessment and treatment for millions of people affected by movement disorders like Parkinson’s Disease (PD). However, most sensor-based gait monitors fail to detect subtle yet clinically relevant abnormalities, such as reduced arm swing or shuffling of steps.
National Institutes of Health (NIH) actively supports translational research efforts aimed at decreasing the burden of PD and other neurological diseases. With funding from NIH, we have developed a novel approach consisting of advanced body-worn sensors and machine-learning algorithms to monitor, in real-time, the quality of gait movements in activities of daily living.
Join Bhawna Shiwani & John Letizi this October to go behind the scenes of this NIH-supported study, and learn:
- What is the “fingerprint” of abnormal gait, and how we can detect it automatically
- Why high-fidelity EMG + IMU sensors are crucial for measuring quality of movement
- How clinically relevant data metrics from wearable technology can revolutionize health care research today & tomorrow
If you are not able to attend the live webinar, make sure to register for the Autonomous Gait Monitoring in Parkinson’s Disease webinar and we will send you a recording following the event.