An app uses smartphone sensors to detect onset signs of Parkinson's in users which could lead to earlier treatment options.
In a world where disease has become increasingly manageable and avoidable, it is undeniable that technology and innovation has played a major part in this. We have seen technology step in and help prevent disease. For example, the sKan device can detect skin cancer. The low-cost device uses temperature sensors to detect areas of tissue which heat up faster, a possible sign of cancerous cells. Innovation has not just helped us to identify the onset of disease, but also to help those suffering from illnesses. An example of this is WalkAid. This is a wearable which is designed to help those with Parkinson’s disease walk. The device works using a visual cue device and tactile insoles. i-PROGNOSIS goes one step further and combines aspects of both these inventions, by detecting the onset of Parkinson’s disease.
The app primarily uses smartphones and smartwatches to detect early symptoms of the disease. Users download the app to their device, and use it for everyday functions, such as phone calls, messages and photos. The app takes advantage of the sensors already present in smartphones, such as the accelerometer and gyroscope, to detect any early minor tremors. i-PROGNOSIS also uses the microphone to spot signs of voice degradation.
Furthermore, the app analyses photos taken of the user’s face to identify any facial features that can relate to Parkinson’s. Finally, the device tracks user’s touching patterns in order to sense early tremors. With a smartwatch, additional information can be collected, such as activity, heart rate, sleep patterns and skin temperature. Should there be indications of the disease, users can purchase additional devices, such as a smart plate and belt to collect more relevant data. Analysing all this data, i-PROGNOSIS advises the user when to seek medical advice.
This app is an innovative way to make use of the existing technology present in smartphones. Perhaps this sophisticated use of sensors could help detect early symptoms of other diseases. Could this be the beginning of a data-driven alternative to traditional medical practice?