Testing and Validating Powered Attendant-Propelled Wheelchairs with NI CompactDAQ and LabVIEW

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"We used NI CompactDAQ and LabVIEW to create a solution that simultaneously acquires and logs data as well as synchronises I/O channels—far exceeding our expectations."

- Derrick Boampong, PAMELA Facility

The Challenge:
Creating an assistive control system for an attendant-propelled wheelchair that easily adapts to an individual’s capability and provides sufficient support for the attendant.

The Solution:
Using the NI CompactDAQ platform and NI LabVIEW software to develop and implement an assistive control method based on the individual’s force velocity (FV) relationship when propelling the wheelchair across difficult terrains.

Author(s):
Derrick Boampong - PAMELA Facility
Nick Tyler - PAMELA Facility, UCL
Catherine Holloway - PAMELA Facility, UCL
Tatsuto Suzuki - PAMELA Facility, UCL

 

The Pedestrian Accessibility and Movement Environment Laboratory (PAMELA), the only multisensory pedestrian laboratory in the world, is part of the Accessibility Research Group (ARG) within the Department of Civil, Environmental, and Geomatic Engineering at University College London (UCL).

 

Fig.1 UCL PAMELA Facility

 

The facility encompasses a computer-controlled configurable platform, variable lighting, and dynamic ambisonic sound systems (Figure 1). Researchers at PAMELA use eye tracking, body motion analysis, inertial motion, pressure measurement, video and audio recording systems, EEG, EMG, galvanic skin response, force transducers, force plates, and other equipment to obtain and analyse data linked together using NI LabVIEW software.

Project Background

Due to advances in medicine, life expectancy in the United Kingdom is increasing. However, 85 percent of people over the age of 70 suffer from mobility impairments and 25 percent of people who suffer from cerebrovascular diseases and osteoarthritis need to use assistive devices due to poor mobility. An attendant-propelled wheelchair, commonly used by elderly people with limited mobility, plays a key role in improving accessibility.

As the average age of the population increases and the distribution of young working people to retired individuals shifts to favor retirees, there are fewer young wheelchair attendants. Often spouses, who may themselves be elderly, work as attendants. Depending on the wheelchair type and occupant weight, a wheelchair can weigh 100kg or more, making it difficult to maneuver and possibly unsafe for the attendant and occupant. Also, an attendant’s efforts to keep an occupant safe while traveling uphill or on an uneven road could result in overexertion or pain in the back, shoulder, or elbow. We need to reduce wheelchair propelling loads for attendants who assist the disabled.

Attaching powered assistive devices to wheelchairs is one way to reduce the propelling load. Many such powered assistive devices succeed in increasing wheelchair maneuverability. However, they do not optimise the relationship between assisting force and individual capability to reduce energy expenditure of the attendant, a topic that is growing in popularity as an area where assisting control systems can aid society. This is mainly because the control method commonly used in the assisting device is simply that the assisting force is proportional to the propelling force. The pushing force of elderly people in particular varies, making it difficult to adapt the current control system parameters to the individual’s capability.

System Description

We proposed an assist-as-needed control system based on the individual’s force velocity (FV) relationship that included the following features:

  • Assist if propelling force is insufficient at the attendant’s natural propelling capability
  • Use the attendant’s natural propelling capability at a low-propelling load to reduce energy consumption
  • Adapt easily to an individual’s capability or propelling performance

We investigated this high-performance controller to assist the propelling behavior of the attendant posed by the propelling load of the wheelchair on a flat level surface at three longitudinal slopes of 6.5 percent (3.6 deg), 9.0 percent (5.0 deg), and 12.0 percent (6.9 deg) on the PAMELA platform (Figure 2).

Figure 2. PAMELA Platform Slopes Configuration

 

We instrumented an NHS 9L standard wheelchair with two polyurethane front castors and rear wheels with 6-axis MC3A force sensors to measure the attendant propelling force, 500 p/r rotary encoders to measure wheelchair velocity, a distance sensor to measure the attendant’s distance from the wheelchair, and sandals fitted with foot switches to measure the timing of contact with the feet. Signals from all the sensors were wired into an NI 9205 C Series module and slotted into the NI cDAQ-9174 chassis. We developed a VI (Figure 3) to acquire and log the appropriate data from the signals.

 

Figure 3. Instrumented WC Acquiring & Logging Data VI Front Panel

 

Experiment participants pushed the instrumented wheelchair on a level surface and on the three different longitudinal slopes and each individual’s pushing capability was calculated from the applied forces, wheelchair velocity, walking patterns, and posture. The experimental results, which were measured by LabVIEW, showed that an attendant’s natural propelling force was within 10 percent to 30 percent of their maximum force and decreased in proportion with walking speed. The amount of effort (applied force) applied by the attendants increased as the load of the wheelchair (up to 96 kg) and slope gradient (up to 12 percent) increased. In addition, attendants needed large step length and low cadence to exert large pushing forces.

 

Figure 4. Powered Instrumented Wheelchair

 

Based on the initial experiments, we created FV relationships for each person using plots of the force they applied to achieve a given velocity and deriving the relationship between these parameters. We set the assisting boundary at 30 percent of the propelling force used by attendants and fitted the rear wheels of the NHS standard instrumented wheelchair (Figure 4) with DC electric motors and a chain drive system (Figure 5). Motor drivers with shunt regulators fitted on the back of the wheelchair seat controlled the motors. We added the NI 9263 output module to the initial NI system to supply the calculated FV output voltage to the electric motors to directly power the rear wheels when needed. It was crucial to synchronise the I/O signals to successfully test and validate the control system. We could only do this with NI hardware and LabVIEW.

 

Figure 5. Motorised Rear Wheel

 

We created a new VI (Figure 6) to include the assisting control rule that calculates and generates the assisting force when the attendant’s propelling force exceeds a boundary defined by an FV relationship from the initial experiments. We used the VI to test the proposed FV relationship method and the commonly used proportional (P) assisting controller. The P controller generated the assisting forces from the product of the attendant forces and the assist gain. We asked the attendants to repeat the initial experiments with the help of the assisting controllers. The flexibility of LabVIEW meant we could easily set and adjust the assisting boundary and gain to the individual’s propelling performance to make propelling easier on steep slopes.

 

Figure 6. Powered Instrumented WC VI Front Panel

 

The experiments showed that the FV control method does not provide assistive force when under the assisting boundary, unlike the P control method which is ‘always on.’ Instead, the new FV control system provides the assisting forces only when the attendant propelled at a force higher than the assisting boundary defined by their FV relationship.

System Benefits

The control system we created only uses the attendant’s propelling power up to the assisting boundary, which is easily adjustable to the individual’s propelling performance. Therefore, the system ensures that the assisting force is used only when needed and significantly reduces the amount of electrical energy used. This is a major advantage compared to commonly used proportional assisting control systems. Furthermore, an attendant can push the wheelchair around as a form of moderate exercise, with the option of using the assisting power when required.

The improvements achieved through this study provide sufficient assisting forces with lower energy consumption, reduce the possibility of injuries, and eases the attendant’s burden during long-distance driving. This improves the quality of life of the disabled occupant and the attendant, particularly the elderly, who can more easily maneuver the wheelchair outside and across difficult terrains.

The control system can also optimise other assisting systems, rehabilitation systems, and training systems to individual capability.

NI Software and Hardware Exceed Expectations

We used NI CompactDAQ and LabVIEW to create a solution that simultaneously acquires and logs data as well as synchronises I/O channels—far exceeding our expectations. LabVIEW offered easy integration with third-party hardware and, as new users, we found the graphical programming environment intuitive to use.

The user-friendly front panel helped us incorporate parameters such as assist gain, time constant, and the FV relationship to easily adjust and control assisting motors on the wheelchair. Without this feature, we could only have tested the proposed control rule on a simulated wheelchair.

Author Information:
Derrick Boampong
PAMELA Facility
PAMELA Facility, UCL, Unit1 Bush Industrial Estates, Station Road, Tufnel Park
London N19 5UN
United Kingdom
Tel: 0207 281 2976
derrick.boampong@ucl.ac.uk

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