Kinematic Analysis and Evaluation of Wheelchair Mounted Robotic Arms

This paper focuses on kinematic analysis and evaluation of wheelchair mounted robotic arms (WMRA). It addresses the kinematics of the WMRA with respect to its ability to reach common positions while performing activities of daily living (ADL). A procedure is developed for the kinematic analysis and evaluation of a WMRA. In an effort to evaluate two commercial WMRAs, the procedure for kinematic analysis is applied to each manipulator. Design recommendations and insights with regard to each device are obtained and used to design a new WMRA to overcome the limitations of these devices. This method benefits the researchers by providing a standardized procedure for kinematic analysis of WMRAs that is capable of evaluating independent designs.

This paper focuses on kinematic analysis, evaluation and design of wheelchair mounted robotic arms (WMRA). It addresses the kinematics of the WMRA with respect to its ability to reach common positions while performing activities of daily living (ADL). A procedure is developed for the kinematic analysis and evaluation of WMRAs. In an effort to evaluate two commercial WMRAs, the procedure for kinematic analysis is applied to each manipulator. Design recommendations and insights with regard to each device are obtained and used to design a new WMRA to overcome the limitations of these devices. This method benefits the researchers by providing a standardized procedure for kinematic analysis of WMRAs that is capable of evaluating independent designs

Wheelchair-mounted robotic arms support people with upper extremity disabilities with various activities of daily living (ADL). However, the associated cost and the power consumption of responsive and adaptive assistive robotic arms contribute to the fact that such systems are in limited use. Neuromorphic spiking neural networks can be used for a real-time machine learning-driven control of robots, providing an energy efficient framework for adaptive control. In this work, we demonstrate a neuromorphic adaptive control of a wheelchair-mounted robotic arm deployed on Intel’s Loihi chip. Our algorithm design uses neuromorphically represented and integrated velocity readings to derive the arm’s current state. The proposed controller provides the robotic arm with adaptive signals, guiding its motion while accounting for kinematic changes in real-time. We pilot-tested the device with an able-bodied participant to evaluate its accuracy while performing ADL-related trajectories. We further demonstrated the capacity of the controller to compensate for unexpected inertia-generating payloads using online learning. Videotaped recordings of ADL tasks performed by the robot were viewed by caregivers; data summarizing their feedback on the user experience and the potential benefit of the system is reported.

Purpose The increasing prevalence of upper limb dysfunctions due to stroke, spinal cord injuries, and multiple sclerosis presents a critical challenge in assistive technology: designing robotic arms that are both energy‑efficient and capable of effectively performing activities of daily living (ADLs). This challenge is exacerbated by the need to ensure these devices are accessible for non‑expert users and can operate within the spatial constraints typical of everyday environments. Despite advancements in wheelchair‑mounted robotic arms (WMRAs), existing designs do not achieve an optimal balance—minimizing energy consumption and space while maximizing kinematic performance and workspace. Most robotic arms can perform a range of ADLs, but they do not account for outdoor environments where energy conservation is crucial. Furthermore, the need for WMRAs to be compact in idle configurations—essential for navigating through doors or between aisles—adds another layer of complexity to their design. This paper addresses these multifaceted design challenges by proposing a novel objective function to optimize the link lengths of WMRAs, aiming to reduce energy consumption without compromising the robots’ operational capabilities. Materials and Methods To achieve this optimization, the scatter search method was employed, incorporating considerations of collision and singularity avoidance while ensuring the arm remains compact when not in use. The proposed design was evaluated through simulations and experimental validation with both healthy subjects and individuals with lower limb dysfunctions. Results and Conclusions The optimized WMRA demonstrated significant improvements in energy efficiency and spatial adaptability while maintaining the required kinematic performance for ADLs. The validation process confirmed the practical applicability of the proposed design, highlighting its potential to enhance mobility and independence for individuals with upper limb impairments. This study contributes to the field of disability and rehabilitation by providing a structured approach to designing assistive robotic arms that better align with real‑world constraints and user needs.

In a household setting, a wheelchair-mounted robotic arm (WMRA) can be useful for assisting elderly and disabled individuals. However, the current WMRA can only perform movement and grasping tasks through joystick remote control. This method results in low efficiency due to poor coordination between the mobile platform and the robotic arm as well as the numerous operational steps required. To improve the efficiency and success rate of the robot in task execution, this paper proposes a parking location optimization method that combines the occupied grid map (OGM) and the inverse reachability map (IRM). Firstly, the SLAM algorithm is used to collect environment information, which is then stored in the form of an occupied grid map. The robotic arm workspace is then gridded, and the inverse reachability map is calculated based on the grasping pose of the target object. Finally, the optimal position of the mobile platform is obtained by comparing the optimal location point in the inverse reachability map and the obstacle information in the occupied grid map. This process achieves base placement optimization based on the grasping pose. The experimental results demonstrate that this method reduces the user operation time by 97.31% and overall task completion time by 40.57% when executing household environment tasks compared with the joystick control, increasing the range of executable tasks compared with the algorithm of the EL-E robot and reducing task completion time by 23.48% for the same task. This paper presents a parking location optimization method that can improve the grasping efficiency of the robotic arm and achieve parking location position selection for the WMRA in a household environment.

A wheelchair-mounted robotic arm (WMRA) was designed and built to meet the needs of mobility-impaired persons with limitations of upper extremities, and to exceed the capabilities of current devices of this type. The mechanical design incorporates DC servo drive, with actuator hardware at each individual joint, allowing reconfigurable link lengths. It has seven degrees of freedom and uses a side mount on a power wheelchair. The control system allows coordinated Cartesian control, and offers expandability for future research, such as coordinated motion with the wheelchair itself. This paper discusses the current state of the art in WMRAs; describes the design goals and user requirements for this device; explains the component selection process; discusses details of the mechanical design, electrical system and low-level controller; covers manufacturing concerns; and describes the testing of the completed arm. Further improvements are also suggested

A wheelchair-mounted robotic arm (WMRA) system was designed and built to meet the needs of mobility-impaired persons with limitations of upper extremities, and to exceed the capabilities of current devices of this type. The control of this 9-DoF system expands on the conventional control methods and combines the 7-DoF robotic arm control with the 2-DoF power wheelchair control. The 3-degrees of redundancy are optimized to effectively perform activities of daily living (ADLs) and combine wheelchair mobility and arm manipulation to overcome singularities, joint limits and some workspace limitations. The control system is designed for teleoperated or autonomous coordinated Cartesian control, and it offers expandability for future research. Several interchangeable user interfaces were implemented in the design, including a Brain Computer Interface (BCI). That BCI system was modified and integrated to the control of the WMRA system for users who are totally paralyzed or “locked-in” and cannot use conventional augmentative technologies, all of which require some measure of muscle control. Testing and data collection were performed on human subjects, and the design, various optimized control methods and test results are presented in this paper. According to the 2006 US Census Bureau report (US Census Bureau, 2002), about 51.2 million Americans (18.1 percent of the US population) had some level of disability and 32.5 million of them (11.5 percent) had a severe disability. About 10.7 million Americans older than 6 years of age needed personal assistance with one or more activities of daily living (ADL). This work focuses on people who have limited or no upper extremity mobility due to spinal cord injury or dysfunction, or genetic predispositions, or people who are “lockedin” (e.g., by end-stage amyotrophic lateral sclerosis, brainstem stroke, or severe polyneuropathy). Robotic aides used in these applications may vary from advanced limb orthosis to robotic arms (Reswick, 1990). A wheelchair mounted robotic arm can enhance the manipulation capabilities of individuals with disabilities that are using power wheelchairs, and reduce dependence on human aides. 3

Purpose Despite the benefits of wheelchair-mounted robotic arms (WMRAs), occupational therapists are not yet widely involved in the recommendation or implementation of these assistive devices. The purpose of this study was to investigate and compare the current practices and perspectives of occupational therapists who had and had not recommended a WMRA on the recommendation, training, and implementation of WMRAs. Methods This was a descriptive cross-sectional study. An online survey was sent to Canadian, European, and American occupational therapists who had or had not worked with WMRAs. Respondents were asked close-ended questions about their experience, role, barriers, motivations, and future needs regarding WMRAs. We compared results between respondents who had and had not recommended WMRAs using descriptive statistics. Results Ninety-three North American and European occupational therapists completed the survey. Of those, 29 (31.2%) had recommended a WMRA, mostly the JACO robotic arm (n = 26, 89.7%) in rehabilitation centres (n = 18, 62.1%). Their perspectives on their role and barriers related to WMRAs were similar to those who had never recommended a WMRA. All respondents recognised the relevance of occupational therapists’ contribution, and most reported interest in WMRAs (n = 76, 81.7%). However, many barriers emerged, mainly related to limited funding (n = 49, 76.6%), lack of training and knowledge (n = 38, 59.4%), and resource constraints (n = 37, 54.4%). Future needs identified matched these barriers. Conclusion This survey provides novel insight into occupational therapists’ perspectives on WMRAs. It highlights that health professionals need to have easier access to funding, formal training, and resources to support their involvement with WMRAs. Implications for rehabilitation Most occupational therapists are interested in working with WMRAs, considering the potential of these devices to support individuals with upper extremity impairments in their daily activities. They also recognise their unique contribution to the assessment, recommendation, and implementation process among multidisciplinary teams. WMRA recommendation is relevant in various clinical settings and with a wide range of client populations. Nevertheless, it appears that occupational therapists working with adults, in rehabilitation centres or specialised clinics, may have more opportunities to get involved in this process and to attend formal training on this technology, as compared to other settings. Many barriers remain, impeding occupational therapists’ role in the recommendation and implementation of WMRAs. Addressing these barriers may increase the number of devices that are successfully adopted and utilised by individuals with upper extremity impairments. In particular, future research and health policies should focus on access to sufficient funding, formal training, and resources for occupational therapists relative to their role in recommending and implementing WMRAs.

Electric wheelchair-mounted robotic arms can help patients with disabilities to perform their activities in daily living (ADL). Joysticks or keypads are commonly used as the operating interface of Wheelchair-mounted robotic arms. Under different scenarios, some patients with upper limb disabilities such as finger contracture cannot operate such interfaces smoothly. Recently, manual interfaces for different symptoms to operate the wheelchair-mounted robotic arms are being developed. However, the stop the wheelchairs in an appropriate position for the robotic arm grasping task is still not easy. To reduce the individual’s burden in operating wheelchair in narrow spaces and to ensure that the chair always stops within the working range of a robotic arm, we propose here an operating system for an electric wheelchair that can automatically drive itself to within the working range of a robotic arm by capturing the position of an AR marker via a chair-mounted camera. Meanwhile, the system includes an error correction model to correct the wheelchair’s moving error. Finally, we demonstrate the effectiveness of the proposed system by running the wheelchair and simulating the robotic arm through several courses.

In this paper, we present a novel vision based interface for selecting an object using a Brain Computer Interface (BCI), and grasping it using a robotic arm mounted to a powered wheelchair. As issuing commands through BCI is slow, this system was designed to allow a user to perform a complete task using the robotic system via the BCI issuing as few commands as possible, without losing concentration on the stimuli or the task. A scene image is captured by a camera mounted on the wheelchair, from which a dynamically sized non-uniform stimulus grid is created using edge information. Dynamically sized grids improve object selection efficiency. Oddball paradigm and P300 event related potentials (ERP) are used to select stimuli, the stimuli being each cell in the grid. Once selected, object segmentation and matching is used to identify the object. Then the user, using BCI, chooses an action to be performed on the object via the wheelchair mounted robotic arm (WMRA). Tests on 6 healthy human subjects validated the functionality of the system. An average accuracy of 85.56% was achieved for stimuli selection over all subjects. With the proposed system, it took the users an average of 5 commands to grasp an object. The system will eventually be useful for completely paralyzed or locked-in patients for performing activities of daily living (ADL) tasks.

Purpose This review examines wheelchair-mounted robotic arms (WMRAs) as an emerging assistive technology that enhances independence and quality of life for individuals with upper- and lower-limb disabilities. By enabling independent performance of activities of daily living (ADLs), WMRAs hold significant promise for disability and rehabilitation. The article aims to critically evaluate the state of the art in WMRA research and development, identifying persistent challenges and highlighting promising innovations. Materials and Methods The review systematically analyzes literature on WMRAs published between 2001 and 2025. The analysis emphasizes design specifications, degrees of freedom, actuation methods, control strategies, and performance evaluations. A comparative synthesis is conducted to assess how existing systems support ADL execution, while also integrating technical considerations with user-centered outcomes. Results and Conclusions The findings indicate that current WMRA designs face significant limitations, including restricted workspace coverage, inadequate gripper dexterity, suboptimal kinematic configurations, limited payload capacity, high cost, and lack of modularity. Safety mechanisms remain underdeveloped, creating barriers to broader adoption. Nevertheless, advancements in AI-driven control systems, modular design strategies, and integration with complementary assistive technologies demonstrate promising progress. The review concludes that WMRAs have substantial potential to improve autonomy and daily functioning for individuals with disabilities. Addressing technical and practical shortcomings is essential to ensure successful real-world deployment. These insights contribute to disability and rehabilitation research, as they highlight pathways to enhance accessibility, safety, and cost-effectiveness in assistive technologies that support independent living.

Wheelchair-mounted robotic arms have been commercially available for a decade. In order to operate these robotic arms, a user must have a high level of cognitive function. Our research focuses on replacing a manufacturer-provided, menu-based interface with a vision-based system while adding autonomy to reduce the cognitive load. Instead of manual task decomposition and execution, the user explicitly designates the end goal, and the system autonomously retrieves the object. In this paper, we present the complete system which can autonomously retrieve a desired object from a shelf. We also present the results of a 15-week study in which 12 participants from our target population used our system, totaling 198 trials.

Wheelchair-mounted robotic arms have been commercially available for a decade. In order to operate these robotic arms, a user must have a high level of cognitive function. Our research focuses on replacing a manufacturer-provided, menu-based interface with a vision-based system while adding autonomy to reduce the cognitive load. Instead of manual task decomposition and execution, the user explicitly designates the end goal, and the system autonomously retrieves the object. In this paper, we present the complete system which can autonomously retrieve a desired object from a shelf. We also present the results of a 15-week study in which 12 participants from our target population used our system, totaling 198 trials.

People suffering from severe disability like spinal cord injury dysfunctions are unable to control a wheelchair via a common joystick interface. For this target group we have developed a robotic wheelchair able to perform autonomous navigation and operate on different driving modes depending on the disability type of its user. Moreover, a 7-DoF wheelchair - mounted robotic arm (WMRA) was added to the 2-DoF nonholonomic wheelchair platform. In this paper, an image - based visual servoing (IBVS) approach is described with a Speeded Up Robust local Features detection (SURF) using eye-in-hand and eye-to-hand camera configuration for combined control of mobility and manipulation of the 9-DoF WMRA system to execute activities of daily living (ADL) autonomously. Selecting a control mode is done either by voice or by the touchscreen. Experiments with human users highlight advantages of augmentation in wheelchairs.

Wheelchair Mounted Robotic Arms (WMRA) can be used by people with severe motor skill impairment, such as SMA (Spinal Muscular Atrophy), Cerebral Palsy etc…, in order to achieve daily life tasks. Many of those systems have been presented in literature and are available on the market but they are really expensive and bulky.