The smart system analyses motor dysfunction in patients with neurological conditions. The objective mobility assessment of a Parkinson's patient performing the finger tapping test can be done automatically. Artificial intelligence algorithms track in detail a patient's body and compute the amplitude and rate of the tapping automatically, using a simple mobile phone video.
Stride length estimation
State-of-the-art computer vision algorithms recognise gait disorder automatically. Using nothing more than a video and no markers, the system can replicate gait lab functionality and estimate relevant gait metrics, such as stride length and stride length variability.
Patient 3D pose is infered automatically using a simple video, making it possible to predict bad posture that can lead to back pain.
Falls risk estimation
Mobility metrics extracted automatically from video can be used to predict high risk of falling over. Falling has catastrophic effects on elderly people, but can be predicted based on abrupt changes in gait metrics (stride length, stride length variability, mobility etc.).
Falls in the elderly can be detected using video monitoring. Cutting edge artificial intelligence algorithms track people and recognise dangerous events, such as falls, alerting families, carers or health specialists when they happen.
Patients are automatically monitored for adverse events and the system notifies carers or clinicians in time to prevent dangers. Algorithms are able to automatically track 2D posture and detect falls, medication intake, climbing out of bed and other relevant events.