Hand tremors from diseases such as essential tremor, Parkinson’s disease, Wilson’s disease, dystonia and others affect tens of millions of people around the world, and the neurological and genetic basis for many tremors is still yet to be understood. Patients suffer physically, often unable to write and practice art, as well as socially, with tremors giving rise to more social anxiety. Unfortunately, there are relatively few ways for individuals and doctors to quickly and reliably track tremor progression over time.

Recently, I met a neuroscientist named Marianne Stephans at a hackathon in San Francisco connecting people working in the neuroscience field with technologists. We worked together afterwards to build a movement sonification application – a method for giving sound feedback for physical movement, related to research she does connecting motor control and auditory memory/control. I found myself inspired by the problem set, given the number of people suffering from these types of motor control problems.

With better tremor measurement and tracking using Leap Motion, I believe research could progress faster aiding in the treatment of tremors and doctors could have a more efficient tool for quantifying tremor.

Current assessment tools

Most assessment is done manually, with tools that can be cumbersome and provide limited data for reliably quantifying tremor characteristics. These include accelerometers, graphic tablets, mechanical-linkages, and EMG sensors. Most require physically connecting devices to the patient, which can be time-consuming and expensive. Additionally, according to researchers at the Tremor Research Group in 2006:

Tremor rating scales (TRSs) are used commonly in the clinical assessment of tremor, but the relationship of a TRS to actual tremor amplitude has never been quantified. Consequently, the resolution of these scales is unknown, and the clinical significance of a 1-point change in TRS is uncertain.

This continues to be the case.

Measurement resolution

One of the first questions that came up was what sort of movement resolution and reliability was available using the Leap Motion Controller. Fortunately, a research group had done a study on an early developer device and its capabilities in early 2013. Using an industrial precision robotic arm, they found the resolution to be around .2 mm of movement and 1.2 mm after accounting for error. Additionally, the Leap Motion Controller specifies the frame rate at 290+ frames per second, giving plenty of updates for frequency analysis up to 145 Hz based on the Nyquist-Shannon theorem.

Pattern tracing and analysis

Now confident in the device’s capabilities, I set out to build a simple application to track movement over a given space and time. This materialized into a tool to mimic the assessment test used by many professionals, where a patient is asked to draw a pattern on a piece of paper. The pattern is then analyzed and compared to the model pattern to quantify tremor.

To do this, the patient is asked to trace a line on the screen with feedback on where the finger is. Once a line tracing is complete, the application calculates average position, deviation, variance, time, and other statistics. This could easily be applied to squares/circles or in three dimensions for more complex patterns like spirals.

One challenge with this approach was visually defining the location of the finger on the screen and how to start the acquisition. It made sense to show an entry and exit zone, so that the app could detect the finger entering the start line and start tracking from there. With a few modifications, the application could be made to track multiple iterations at once and quantify/average all tests.

Frequency analysis

Another important characteristic of hand tremor that clinicians care about is the movement frequency – so my second goal was to use the high-speed capabilities of the Leap Motion Controller to accurately measure hand tremor frequency. I built an application to record the movement data of a patient’s hand being held over the device and quantify the entire hand movement as well as individual digits. After a fixed amount of time, the data is analyzed for frequency components and other movement statistics.

What this means

Though these applications don’t actually solve the problem of tremors, they may be able to bring low-cost assessment to doctors and sufferers, yielding more data from which to analyze and generate solutions. They are simplified tests to demonstrate the concept of testing hand tremor with the device, but foundational in determining a path forward. With more data and a fixed testing procedure, highly accurate tremor analysis could help doctors and researchers find solutions to the problem of tremors. Additionally, a database of patient data could be established and more complex tests could be created to get more specific answers to neurologists’ questions.

Drawbacks

It’s limiting to only be able to use the technology for hand tremors, though future work will likely bring this approach to whole or specific body tracking. Another potential downside is the lack of tactile feedback during a test. Moving a hand in the air without experiencing pushback (such as a pen/paper interaction) may not provide for the best results. Further study is needed.

Feedback

I’m currently reaching out to neurologists and clinicians to gauge and calibrate the value of this application. What is your feedback? Do you know someone suffering from tremors or working in neurology? Would this be valuable for them? Please share and/or ask them what they’d think.

Chris Smith

Chris Smith is a tech entrepreneur helping people use sensors to measure, automate, and test things. He does consulting work for d2itechnologies.com and blogs at christopherlsmith.com.