Power-assisted Wheelchair Research Articles

Efficiency of Robust Observer Design in Power-Assisted Wheelchair Applications

Efficiency of Robust Observer Design in Power-Assisted Wheelchair Applications

Calibration to Differentiate Power Output by the Manual Wheelchair User from the Pushrim-Activated Power-Assisted Wheel on a Force-Instrumented Computer-Controlled Wheelchair Ergometer

To examine the biomechanical demands of manual wheelchair propulsion, it is crucial to determine the wheelchair user’s (WCU) force for propulsion technique parameter calculation. When using a pushrim-activated power-assisted wheelchair (PAPAW) on a wheelchair ergometer, a combined propulsion force from the WCU and PAPAW is exerted. To understand PAPAW’s assistance and distinguish the WCU’s force application from the force exerted by the PAPAW, both propulsion components must be assessed separately. In this study, a calibration of the PAPAW on an ergometer was developed to achieve this separation. The calibration consists of five steps: (I) Collecting data on force and velocity measured from the ergometer, along with electrical current and velocity from the PAPAW. (II) Synchronizing the velocity signals of the wheelchair ergometer and PAPAW using cross-correlation. (III) Calibrating the PAPAW’s electromotors to convert electrical current (mA) to force (N). A product-specific motor constant of 0.30, provided an average ICC of 0.563, indicating a moderate agreement between the raw ergometer data (N) and the motor constant-converted drive-rim (PAPAW) data (from mA to N). (IV) Subtracting the PAPAW’s force signal from the ergometer’s measured force to isolate forces generated by the WCU. (V) Using markerless motion capture to determine and validate the phase of hand contact with the handrim. This technical note provides an example of PAPAW calibration for researchers and clinicians. It emphasizes the importance of integrating this calibration into the development of PAPAW devices to reveal the complex interaction between PAPAW and WCU during wheelchair propulsion.

Power-assist on slope with velocity compensation for attendant-propelled electric wheelchairs

Up to now, numerous electric-powered wheelchairs (EPWs) have been developed for the disabled and the elderly. Among these EPWs, the traditional attendant-propelled power-assist wheelchairs (APAWs) usually run at a preset speed and greatly reduce the attendant's burden. Since the attendant's operating feeling is essential for the widespread use of the APAWs, many control approaches have been studied and developed. In the power-assist of our developed APAW, the speed of the wheelchair is controlled by the attendant's force to keep an appropriate burden with a natural operational feeling. In this study, we clarify the fact that the handle velocity of the APAW inevitably changes when passing through a slope, which causes the attendant to have an uncomfortable feeling. This phenomenon and its result in the uncomfortable feeling have not been reported yet. Consequently, in order to reduce or eliminate such uncomfortable feelings, we construct an APAW model on the slope and propose a velocity compensation method. From simulations and experiments, the validity of the proposed velocity compensation control is verified.

Fuzzy Inference-Based Driving Control System for Pushrim-Activated Power-Assisted Wheelchairs Considering User Characteristics

Pushrim-activated power-assisted wheelchairs that assist the driving power by electric motors are effective in the daily lives of physically disabled people in terms of expanding the range of daily activities and comfortable driving even on rough and uphill roads. Meanwhile, users with unbalanced pushing power of the right and left arms due to some disability have difficulty controlling the wheelchair as he or she requests. For example, users with a lower-right arm strength have difficulty simultaneously driving forward and to the left. Therefore, a power-assist control system that considers the wheelchair operation characteristics of the individual user should be designed. The present article proposes a driving control system of pushrim-activated power-assisted wheelchairs with the target position estimation based on a learning model of individual operation characteristics. An individual estimation model based on fuzzy inference is constructed based on the right- and left-pushing torque information, which is properly revised to improve the position estimation accuracy. In addition, an operation training system with visual presentations to improve the proficiency of the user is effectively used. Experimental results using trial subjects, including a physically disabled person with arthrogryposis multiplex congenita, show that assisted driving intended by users can be realized, which verifies the effectiveness of the proposed driving control system and training system.

System for reduction of the caregiver’s workload using a power-assisted wheelchair

System for reduction of the caregiver’s workload using a power-assisted wheelchair

Performance of Impedance Control-Based Strategies in Power-Assisted Wheelchairs: A Predictive Simulation Study.

Manual wheelchair propulsion is known to be inefficient and causes upper extremity pain, fatigue, and injury. Power-assisted wheelchairs can mitigate these effects through motors that reduce users' effort and load during propulsion. Among the different control strategies proposed to govern the user-wheelchair interaction, impedance control-based ones appear to be the most natural and effective. It can change the apparent dynamical properties of the wheelchair, particularly mass and friction, and automatically compensate for external disturbances such as terrain conditions. This study investigates the advantages and disadvantages of this control strategy employing predictive simulations of locomotion with power-assisted wheelchairs in different scenarios. The simulations are generated using a biomechanically realistic model of the upper extremities and their interaction with the power-assisted wheelchair by solving an optimal control problem. Investigated scenarios include steady-state locomotion vs. a transient maneuver starting from rest, movement on a ramp vs. a level surface, and different choices of reference model parameters. The results reveal that the investigated impedance control-based strategy can effectively reproduce the reference model and reduce the user's effort, with a more significant effect of inertia in the transient maneuver and of friction in steady-state locomotion. However, the simulations also show that imposing a first-order, linear reference model with constant parameters can produce disadvantageous locomotion patterns, particularly in the recovery phase, leading to unnecessary energy dissipation and consequent increase in energy consumption from the batteries. These observations indicate there is room for improvement, for instance, by exploring energy regeneration in the recovery phase or by switching reference model nature or parameters along the cycle. To the best of our knowledge, this is the first investigation of impedance control-based strategies for power-assisted wheelchairs using predictive simulations and a realistic, nonlinear model of the user-wheelchair system.

Offline and Real-Time Implementation of a Terrain Classification Pipeline for Pushrim-Activated Power-Assisted Wheelchairs.

Pushrim-activated power-assisted wheelchairs (PAPAWs) are assistive technologies that provide propulsion assist to wheelchair users and enable access to various indoor and outdoor terrains. Therefore, it is beneficial to use PAPAW controllers that adapt to different terrain conditions. To achieve this objective, terrain classification techniques can be used as an integral part of the control architecture. Previously, the feasibility of using learning-based terrain classification models was investigated for offline applications. In this paper, we examine the effects of three model parameters (i.e., feature characteristics, terrain types, and the length of data segments) on offline and real-time classification accuracy. Our findings revealed that Random Forest classifiers are computationally efficient and can be used effectively for real-time terrain classification. These classifiers have the highest performance accuracy when used with a combination of time- and frequency-domain features. Additionally, we found that increasing the number of data points used for terrain estimation improves the prediction accuracy. Finally, our results revealed that classification accuracy can be improved by considering terrains with similar characteristics under one umbrella group. These findings can contribute to the development of real-time adaptive controllers that enhance PAPAW usability on different terrains.

Comparison of Manual Wheelchair and Pushrim-Activated Power-Assisted Wheelchair Propulsion Characteristics during Common Over-Ground Maneuvers.

Pushrim-activated power-assisted wheels (PAPAWs) are assistive technologies that use force sensor data to provide on-demand propulsion assistance to manual wheelchair users. However, available data about kinetic and kinematic of PAPAW use are mainly limited to experiments performed on a treadmill or using a dynamometer. In this work, we performed experiments to gather kinetics of wheelchair propulsion and kinematics of wheelchair motion for a variety of over-ground wheelchair maneuvers with a manual wheelchair with and without PAPAWs. Our findings revealed that using PAPAWs can significantly reduce the propulsion effort and push frequency. Both linear and angular velocities of the wheelchair were significantly increased when using PAPAWs. Less force and push frequency could potentially reduce risk of chronic upper limb injury. Higher linear velocity could be desirable for various daily life activities; however; the increase in the angular velocity could lead to unintended deviations from a desired path. Future research could investigate PAPAW controllers that amplify the desired intentions of users while mitigating any unwanted behaviours.

Modular Segmented Motor for Power-Assist Wheelchairs: Proof of Concept

This paper presents an analysis of the opportunity to increase the price availability of small electric vehicles, such as electric scooters, such as bicycles and wheelchairs, by applying expandability and modularity principles to their motors. Assuming that, in many cases, small electric vehicles are brought to the market in several power/price versions, the authors of this report evaluate the possibility of combining different numbers of electromechanical modules while, at the same time, maintaining the unity of the entire drive/motor scheme, thus making the mentioned expandability possible. Power-assist wheelchairs are taken as an example of the application, where such expandability is reasonable. The application provides a price reduction for the less powerful wheelchairs in the case of less severe disabilities. To start, the authors briefly compare multidrive schemes that ground the principle of modularity at the electromechanical level. Then, they outline a radially segmented motor concept and discuss this concept using the example of a permanent magnet synchronous motor. In particular, they propose a methodology for the calculation of its parameters and calculate the particular design details of such a motor. The motor is then analyzed with the help of its mathematical model, as well as experimentally. This tentative evaluation of two 50 W segments (of a 300 W 6-segment motor) proves that the proposed segmented modularity concept is feasible, and that it requires a more detailed consideration of the parameters and the other implementation aspects (power driver, control, cooling) of the given synchronous motor. Moreover, the concept might be successfully utilized in the designs of other motor types.

Robust observer-based tracking control under actuator constraints for power-assisted wheelchairs

Power-assisted wheelchairs (PWA) is an important growing market. The goal is to provide electrical assistive kits that are able to cope with a large family of disabled people and to equip a large variety of wheelchairs. This work is made in collaboration with Autonomad Mobility, a company that designs the hardware and sells Power-Assistance kits for wheelchairs. Several crucial issues arise, e.g. how to assist any Person with Reduced Mobility (PRM)? How to detect user’s intentions? how to cope with the lack of system information due to excessive sensor costs. Effectively, due to the variety of wheelchairs and the different unknown PRM characteristics (mass, height, force, etc.) and pathologies, it is unrealistic to provide a solution using a precise modeling of the whole system including the wheelchair, the PRM and the ground conditions. However, proposing a safe and secure solution is obviously mandatory for this application. In particular, an on-the-market solution should be also smooth and friendly for the end-user. Estimation of the human torques is a first key point to achieve such a solution, which has been already studied in our previous works. This paper exploits these estimation results to propose a robust control law for PWA systems under saturation constraints. These constraints are unavoidable due to regulations on maximum authorized speed. From a control point of view, it resumes to an output feedback control with partially unknown references (desired speed, direction), unknown parameters (wheelchair and PRM masses, available force, ground characteristics) and input constraints. Finding an effective solution for this constrained output feedback tracking control still remains open. In this paper, we propose a two-step control design using quasi Linear Parameter Varying (q-LPV) formulation to solve this challenging control problem, i.e., first design an observer for state and unknown input estimation, and second propose a robust control scheme under parameter variations and input saturations. The control procedure is reformulated as convex optimization problems involving linear matrix inequality (LMI) constraints that can be efficiently solved with standard numerical solvers. Simulations and real-time experiments are proposed to show the effectiveness of the solution.

A Comparison Between Conventional and User-Intention-Based Adaptive Pushrim-Activated Power-Assisted Wheelchairs.

Pushrim-activated power-assisted wheel (PAPAW) users ideally require different levels of assistance depending on activity and preference. Therefore, it is important to design and develop adaptive PAPAW controllers to account for these differences. The main objective of this work was to integrate a user intention estimation framework into a PAPAW and develop personalized adaptive controllers. We performed experiments to gather kinetics of wheelchair propulsion for a variety of daily life wheelchair activities. The propulsion characteristics (i.e., pushrim forces) were used to train intention estimation models and characterize implicit user intentions when performing daily life wheelchair maneuvers. These intentions included moving straight forward, performing a right/left turn, and braking. The intention estimation framework, based on random forest classification algorithms and kinetic features, was implemented and tested in our laboratory-developed PAPAW. This computationally efficient framework was successfully implemented and tested for each participant in real-time. Our results revealed that the real-time user intention predictions were similar to the offline models. The power-assist ratio of each wheel was adjusted based on which user intention was identified. Data collected from four participants provided evidence regarding the effectiveness of using adaptive intention-based controllers. For instance, the propulsion effort was significantly reduced when using an adaptive PAPAW controller. Subjective views of participants regarding the workload of wheelchair propulsion (e.g., physical/cognitive effort) were also gathered. Our findings suggest that rankings of different controllers varied among different participants and across different wheelchair maneuvers, indicating the need for customized adaptive controllers to fit different users' activities and preferences.

A Comparison Between Conventional and Terrain-Specific Adaptive Pushrim-Activated Power-Assisted Wheelchairs.

Pushrim-activated power-assisted wheels (PAPAWs) are assistive technologies that provide on-demand propulsion assistance to wheelchair users. In this study, we aimed to develop an adaptive PAPAW controller that responds effectively to changes in environmental conditions (e.g., type of surface or terrain). Experiments were conducted to collect kinematics of wheelchair motion using a frame-mounted inertial measurement unit (IMU) while performing a variety of wheelchair activities on different indoor/outdoor terrains. Statistical characteristics of velocity and acceleration measurements were extracted and used to develop a terrain classification framework to identify certain indoor and outdoor terrains. The terrain classification framework, based on random forest classification algorithms and kinematic features, was implemented and tested in our laboratory-developed PAPAW. This computationally efficient terrain classification framework was successfully implemented and tested in real-time. The power-assist ratio of each wheel was adjusted based on the type of terrain (e.g., more assistance was provided on outdoor terrains). Our findings revealed that propulsion effort (e.g., peak input torque) on asphalt was significantly reduced when using adaptive controllers compared to conventional PAPAW controllers. In addition, subjective views of participants regarding the workload of wheelchair propulsion (e.g., physical/cognitive effort) supported the positive effects of adaptive PAPAW controllers. We believe that the adoption of terrain-specific adaptive controllers has the potential to improve the accessibility of outdoor terrains and to prevent or delay upper extremity joint degeneration or pain.

Data-Efficient Reinforcement Learning for Energy Optimization of Power-Assisted Wheelchairs

The objective of this paper is to develop a method for assisting users to push power-assisted wheelchairs (PAWs) in such a way that the electrical energy consumption over a predefined distance-to-go is optimal, while at the same time bringing users to a desired fatigue level. This assistive task is formulated as an optimal control problem and solved by Feng et al. using the model-free approach gradient of partially observable Markov decision processes. To increase the data efficiency of the model-free framework, we here propose to use policy learning by weighting exploration with the returns (PoWER) with 25 control parameters. Moreover, we provide a new near-optimality analysis of the finite-horizon fuzzy Q -iteration, which derives a model-based baseline solution to verify numerically the near-optimality of the presented model-free approaches. Simulation results show that the PoWER algorithm with the new parameterization converges to a near-optimal solution within 200 trials and possesses the adaptability to cope with changes of the human fatigue dynamics. Finally, 24 experimental trials are carried out on the PAW system, with fatigue feedback provided by the user via a joystick. The performance tends to increase gradually after learning. The results obtained demonstrate the effectiveness and the feasibility of PoWER in our application.

Proposal of Target Position Estimation Type Driving Control and Operation Training System for Power-Assisted Wheelchairs

Proposal of Target Position Estimation Type Driving Control and Operation Training System for Power-Assisted Wheelchairs

Development of a Wireless Surface Electromyography (sEMG) Signal Acquisition Device for Power-Assisted Wheelchair System

Muscle fatigue due to long-term of manual wheelchair propulsion is an issue faced by most of users. A power-assisted wheelchair developed to amplify propulsion force that activated by surface electromyography (sEMG). A sEMG detection wireless device to trigger the assistive system is an advantage to ease installation on user’s body and wheelchair which is developed in this study. A device that operated on Arduino Nano processor connected to 2 (two) Myoware sEMG sensors to record muscles electrical potential (EP), recognise the pattern and activate DC motors wirelessly connected through radio frequency. Data monitoring on personal computer and smart phone associated with Bluetooth to store and ease observation on recorded information. Tested on 1 healthy participant by propelling a manual wheelchair on tiled floor. Developed device’s performance tested and the result are average sampling rate (33.55 ms) and average reading latency was just 12.38 ms. Compared to wired device, sampling rate is faster by % and reading latency slower by 1.04 %. Result demonstrate that developed wireless device would improve in speed in signal reading and enhancement on reading latency is needed to provide a reliable device for power-assisted system in the future.

Towards the Development of a Learning-Based Intention Classification Framework for Pushrim-Activated Power-Assisted Wheelchairs.

There has been a growth in the design and use of power assist devices for manual wheelchairs (MWCs) to alleviate the physical load of MWC use. A pushrim-activated power-assisted wheel (PAPAW) is an example of a power assist device that replaces the conventional wheel of a MWC. Although the use of PAPAWs provides some benefits to MWC users, it can also cause difficulties in maneuvering the wheelchair. In this research, we examined the characteristics of wheelchair propulsion when using manual and powered wheels. We used the left and right wheels' angular velocity to calculate the linear and angular velocity of the wheelchair. Results of this analysis revealed that the powered wheel's controller is not optimally designed to reflect the intentions of a wheelchair user. To address some of the challenges with coordinating the pushes on PAPAWs, we proposed the design of a user-intention detection framework. We used the kinematic data of MWC experiments and tested six supervised learning algorithms to classify one of four movements: "not moving", "moving straight forward", "turning left", and "turning right". We found that all the classification algorithms determined the type of movement with high accuracy and low computation time. The proposed intention detection framework can be used in the design of learning-based controllers for PAPAWs that take into account the individualized characteristics of wheelchair users. Such a system may improve the experience of PAPAW users.

Modular Self-Balancing Battery Charger Concept for Cost-Effective Power-Assist Wheelchairs

The paper deals with a power charger capable of quick simultaneous charging of several unevenly discharged batteries. The charger is designed for use in conjunction with a recently developed power-assist wheelchair composed of two armrest modules associated with wheels—each with its own motor, driver and battery. Uneven discharge of the batteries is very possible in this application. Taking into account the charging power and energy comparable with the most powerful household electrical devices, the refreshing of these batteries and integration of the entire power supply chain into the household grid become a topical and challenging task. Solving of this task requires a special charger that has several channels and can unevenly apply charging power to these channels. At the same time, the charger must not generate current harmonics or reactive power, must operate with good efficiency and satisfy size constraints. In the given research, a configuration of several interleaved isolated single-ended primary-inductor converters is studied. The synthesized mathematical model of the proposed charger provides data about its static and dynamic characteristics while its experimental investigation focuses on operation details (power losses, control features etc.). The obtained results prove that the proposed concept complies with the above-mentioned requirements and can be applied in the discussed application.

Robust Observer-Based Tracking Control Design for Power-Assisted Wheelchairs

Abstract Power-assisted wheelchairs (PAW) are efficient means of transportation for disabled persons. The resulting human-machine system includes several unknown parameters such as the mass of the user or ground adhesion. Moreover, the torque signals produced by the human are required to design a robust assistive strategy, but measuring them with torque sensors increases significantly the cost of the system. Therefore, we propose a robust observer-based assistive controller using a polytopic representation. The closed-loop control design is composed of two elements: a state feedback controller with the full state at the input, and an unknown input observer to estimate human torques and feed them into the obtained controller. The goal is to guarantee an imposed H∞ estimation performance while achieving reference tracking. To achieve the predefined performance, the observer gains are computed by solving an LMI problem. Finally, simulation results validate the control design. The methodology follows patent WO2015173094 issued in 2015 (Mohammad et al. 2015).

A Preliminary Study of a Polarizing Effect Based Torsion Sensor for a Power Assisted Wheelchair Driving Control

A handrim-activated power-assisted wheelchair (HAPAW) is a special wheelchair that combines the advantages of a manual wheelchair with those of an electric-powered wheelchair. In this study, we develop a polarizing-effect-based noncontact torque sensor for a HAPAW. Two main considerations were taken when developing the HAPAW: first, effectively sensing the user-propelled torque applied to the handrim is important, and second, developing a small lightweight driving mechanism consisting of a motor and gears is essential. Therefore, this study presents a new noncontact torque recognition method based on the polarizing effect of light. Two polarizers are placed in parallel on the wheel hub and the handrim to determine the driving intention of the user by sensing the changing intensity of the light passing through the polarizers that is proportional to the handrim movement. A mock-up was fabricated and the changes in light intensity were measured with optical detectors to verify the proposed sensor mechanism. The experimental results are in good agreement with the simulation results. The measured sensor output had enough resolution and accuracy to evaluate the driving intention of the HAPAW user.

Development of Handrim-Activated Power-Assist Wheelchair for Seniors and the Disabled

This paper presents a new handrim-activated power-assist wheelchair (HAPAW) for seniors and the disabled. The HAPAW has all the benefits of manual and electric wheelchairs and is being actively developed for use by the disabled and seniors, because of the global aging trend. The key technological components of a HAPAW are a sensor-embedded driving wheel mechanism for detecting user intention and a balancing algorithm for improved driving performance. In this work, a driving wheel mechanism with a simple non-contact user-intention-detection sensor composed of a magnet and Hall sensor was designed. In addition, a balancing algorithm that uses the measured torque and time difference for driving wheel control was applied. An HAPAW prototype incorporating the designed driving wheel and control algorithm was fabricated, and its performance was tested according to the electric wheelchair standards. In addition, to confirm the power-assist effect, the power-assist wheelchair energy consumption was measured separately for senior and disabled participants and compared with that for a manual wheelchair. Through performance testing and clinical testing, it was confirmed that the developed HAPAW can be successfully employed as an electric wheelchair, with improved driving performance and energy efficiency over those of a manual wheelchair.