Doctors currently diagnose many neurological diseases by observing the gait of a patient; however, many patients feel uncomfortable in the medical surroundings and do not behave naturally. To remove this obstacle to diagnose, CITRIS researchers are developing an automated diagnostic system that will enable various gait and movement disorders to be quantitatively characterized. This system, which measures inertia, will allow patients to collect information at their home and at other locations that reflect their daily routine.

The inertia measuring device works by detecting sudden movements of the upper body when it is out of phase with lower extremity movements. Arm measurements can be taken of patients with Parkinsonian syndromes and may help to reveal the unilateral predominance of any deficit. In patients who have suffered from a stroke, asymmetry of arm-swing may be characteristic, whereas in those with cerebellar disturbances or other non-Parkinsonian movement disorders, the arm movements may be chaotic and disorganized. The findings will thus help to identify the nature of the underlying problem.

In addition, this simple and inexpensive device can be used by clinicians in remote areas without experienced experts available to provide diagnostic or prognostic assistance. The data can also be transmitted instantaneously over the Web to such an expert. The sternum sensor also allows the detection of actual falls of the user in “real time” in the home setting, and thus allow for dispatch of emergency medical services to the home when a patient may not be able to call for them by phone or even by a “lifeline” service.

2009 Update:

Defining the biological features of neuropathologies, in particular Parkinson's disease (PD), requires a system that can accurately quantify features of gait. The PD project team of UCSF neurologists and UC Berkeley engineers have assembled a small (4 cm x 6 cm x 3 cm) and lightweight (39 grams) wireless sensor module that accurately measures the three-dimensional location and rotations of the foot of a walking person. The movements are sampled 100 times per second to allow fine details of gait to be calculated with better than 0.5% accuracy. With preliminary data from ten PD subjects, and using arbitrarily chosen features based on the data collected, three of five patients with PIGD, and five patients without PIGD, were correctly identified . This identification was based both on both numerical parameters that have been previously studied, including cadence, stride length, velocity, and stride-to-stride variability in gait cycle timing, as well as and on new markers, including the height of the foot off the ground, and angles of rotation of the foot during the swing phase and in landing on the ground. It is hypothesized that these parameters may be among the set of biological markers of postural instability and gait disturbances (PIGD). Further work will allow us to utilize our system to determine more systematically the biological markers of PIGD so that it may be possible ultimately to recognize PIGD at a subclinical stage. In addition, we are working with Dr. Jay Han, UCDavis) to use our techniques to quantify the abilities, and change of movement abilities, in boys with Duchene's muscular dystrophy.

Our system can serve as a simple and inexpensive tool to subdivide PD patients into PIGD and non-PIGD groups at an early stage and will therefore be important for treatment and clinical trials and for prognostication purposes. Recognition of PIGD should be possible when the PIGD is still subclinical. In addition, the portability of our system allows for gait measurements to be made in the home or other locations that reflect the daily routine of patients and will therefore allow the more meaningful assessment of patients. Finally, the potential exists for integration of the device into tele-medicine systems, which can save patients time from traveling to medical centers and is a cost-effective way for evaluating patients living in remote areas.