We have invented a diagnostic sensor capable of identifying the unique gait signatures of multiple pathologies, including Parkinson’s disease, Multiple Sclerosis, traumatic brain injuries, Alzheimer’s syndrome and related conditions. Moreover, by employing proprietary sensor data processing algorithms, the measured gait data can be evaluated in real time to provide warnings of impending falls.

Although gait assessment instruments have been demonstrated in the past, our invention will in addition analyze such gait measurements in real time so as to identify the specific clinical conditions exhibited by the test subjects. It can therefore be used as a screening tool for early detection of a wide variety of neurological conditions before their classical symptoms become overtly manifest. We envision the routine screening of populations such as the elderly, athletes and military members by employing a non-invasive, lightweight and compact externally worn instrument.

Our invention has already demonstrated conceptual feasibility using the developmental brassboard sensor shown in Figure 1. This brassboard has been worn by several test subjects (Vista Life and Equinox staff members) to measure gait characteristics and to develop signal processing algorithms for the sensor data stream.

The breakthrough capability of our sensor to provide stability alerts in real time is the result of applying proprietary guidance and control sensor data processing methodologies developed by Equinox for aerospace applications. The sensor uses sensitive, high speed accelerometers and gyroscopic sensors oriented in pitch, yaw and roll axes, providing 6 degrees of freedom real-time information at 150 hz. In addition the sensor also provides 3 axis magnetometer information that is not expected to be essential to our efforts. These devices provide 150 measurement points per second during a gait assessment. We will be able to tune the sensor rates as necessary to support higher rates as needed. Whereas similarly dense data sets recorded by the trials mentioned above have proved daunting for real time evaluation, our invention instead applies Fourier Transform analysis to the sensor data stream, greatly reducing the signal processing burden and the future need for high speed communication in a production device.

Figures 2 and 3 display the unprocessed signal flow recorded during a gait assessment trial run and the Fourier Transform spectrum obtained from the raw pitch, yaw and roll axis accelerometer and gyroscopic data using our proprietary frequency domain signal processing algorithms. The raw data displayed in Figure 2 were collected during completion of one complete stride cycle. These plots of amplitude versus time are similar to those obtained by the investigators cited above. Although interesting in a qualitative sense, such unprocessed plots provide neither predictive information of falls nor diagnostic identification of specific neurological pathologies.

In contrast, the Fourier transform processed stride data shown in Figure 3 reveal the dominant frequencies associated with an individual’s gait. This ensemble of frequencies and their associated amplitudes comprise a unique signature. Empirically determined signature variations characteristic of strokes, imbalances, etc. can be monitored in real time to provide warnings and summon assistance from caregivers.