Enhanced Remote Data Logging for Electric Powered Wheelchairs – Phase II

Principal Investigator/s: Dan Ding, PhD

Co-Investigator/s: N/A

Funding Source: Paralyzed Veterans of America Fellowship

2005-2006

Wheelchairs do not inherently lend themselves to data collection. Virtually all quantifiable performance data has been collected in laboratory environments. Community outcomes and consumer satisfaction research has been carried out with survey methods rather than instrumentation. We are developing a remote data logging device incorporating Global Positioning System (GPS) and wireless communication function. The device will be attached to an electric powered wheelchair collecting travel data of wheelchair users as well as performance data of wheelchairs, i.e. driving distance, speed, frequency of using tilt and recline functions etc, and send out emergency location and wheelchair status through onboard GPS and wireless modem. We have completed the prototype of monitoring the usage of tilt/recline and distance/speed. A series of bench tests will be conducted to test the efficacy of the device. Subject testing will be followed up to further prove the usefulness of the device. Through making full use of the latest available technologies, i.e. the GPS and wireless communication, we will maximally improve wheelchair driving safety and enhance independence of wheelchair users.

We developed a device capable of recording wheelchair usage and activities in real-life settings has been developed, which includes three modules (1) wheel rotation logging module, (2) seating posture logging module, and (3) GPS logging module. The system has been bench tested, and subject testing was also conducted with the first two modules.

The wheel rotation logging module can records time stamps of wheel rotations, and provide activity related data, e.g., traveling distance, velocity, direction, and number of stops during any period of the data collection. It uses a pendulum with a small magnet at the bottom that pivots within the enclosure behind the circuit board. Whenever the wheel rotates, the magnet sweeps in a circle over three miniature reed switches mounted at 120-degree intervals on the back of the circuit board. Time stamps of each sensor event are compressed and stored on the flash memory. The custom code in Matlab computed the mobility characteristic variables of daily distance traveled, average daily speed, and active hours. Activity level variables of total accumulated movement time, number of starts/stops per thousand meters, and maximum period of continuous activities were also calculated using the same Matlab code.

The seating posture logging module includes seven sensors. Three single-axis analog tilt sensors measure the angle of the base, the angle of the seat, and the angle of the back. Three pressure sensors under the seat cushion are used to determine the chair occupancy status and weight shifting activities. A potentiometer measures the seat elevation. Pressure reading calibration was performed before actual data collection at different seat and back angles using a pressure-mapping device, and provided the baseline data for examining the time spent on the seating posture that offers the most pressure relief. The performance of seating posture logging module was also bench tested, where the seating functions including seat tilt, back recline, and seat elevation, were operated with known frequency and durations. There was no deviation between the known frequency and the recorded frequency for all the three functions. The duration deviation is less than 1% for the three functions as well. In addition, subject testing protocol for seating posture logging module was also conducted based upon the approval by the Institutional Review Board at the University of Pittsburgh. Data were collected from three wheelchair users for a period of 10 to 14 days.

A standard off-the-shelf GPS board (u-blox Ltd.) was used to develop the GPS logging module which collects traveling information of wheelchair users (see Figure 2). Several bench trials were conducted using a manual wheelchair with both GPS and the wheel rotation logging device. In one trial, the investigator tested the system for about two hours and all the travel routes were recorded by GPS. A custom MATLAB program was used to analyze the data from GPS and the wheel rotation logging device to obtain driving data indoor versus outdoor, and the use of motor vehicle transportation as well. The following manuscript on the GPS module was submitted to 2006 RENSA conference.