Wheelchair-mounted robotic arms: analysis, evaluation and development

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.

There has been significant progress in bringing commercially-viable wheelchair mounted robotic arms (WMRA) into the marketplace in the past 30 years. This paper focuses on kinematic analysis and evaluation of such robotic arms. 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 wheelchair mounted robotic arm. In addition to developing the analytical procedure, the manipulator is evaluated, and design recommendations and insights are obtained. Current commercially-available wheelchair mountable robotic manipulators have been designed specifically for use in rehabilitation robotics. In an effort to evaluate two commercial manipulators, the procedure for kinematic analysis is applied to each manipulator. Design recommendations with regard to each device are obtained. This method will benefit the researchers by providing a standardized procedure for kinematic analysis of WMRAs that is capable of evaluating independent designs.

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.

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.

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.

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: Occupational Therapy Perceptions and Practices

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.

As the population aging becomes more severe, the development of Wheelchair Mounted Robotic Arms (WMRA) has gained greater attention. There are remaining issues that haven't been properly tackled, such as human-robot interaction and real-time performance. In this paper, laser pointer is used to facilitate the interaction between human and the WMRA, and an improved target matching method is proposed for laser point detection in home environment. Firstly, the laser point's characteristics are amplified through the use of channel separation technique and reflective materials. Then, the laser point is separated from the image using background difference method. Finally, through ASUS Xtion, the distance between laser point and the centroid coordination of object is calculated, and then Kinova Jaco robotic arm is used for grasping. The experimental result shows that the algorithm can effectively detect laser point in the home environment, and as a human-robot interaction, the robotic arm successfully completes the task of demo grasp.

BACKGROUND Tetraplegia, often resulting from cervical spinal cord injury (SCI), may lead to significant motor and sensory loss, severely impacting independence and quality of life. Assistive technologies (ATs), such as wheelchair-mounted robotic arms (WMRAs), offer potential to enhance autonomy in daily living. However, adoption remains limited due to high costs, complex controls, and insufficient end-user involvement. Robust evidence on their real-world effectiveness, particularly post-hospitalisation, is still lacking. OBJECTIVE This study explores the real-life use of a WMRA for individuals with tetraplegia. It aims to evaluate its support in activities of daily living (ADLs), assess usability and satisfaction, and conduct a preliminary health economic analysis comparing cost-effectiveness and quality of life outcomes with standard care. METHODS This study will be conducted in post-hospitalisation settings in Switzerland. Up to 15 participants with upper limb impairments (SCI C0–Th1, AIS A–D) using powered wheelchairs will be recruited. They will use the robotic arm for six consecutive days. An equal number of participants will be recruited for the economic analysis group. A mixed methods approach will combine quantitative data collected via standardised questionnaires (PSSUQ, NASA-TLX, EQ-5D-5L, VAS, aCOMP, CSSRI-EU) at baseline and post-intervention, along with qualitative feedback gathered through an informal questionnaire and semi-structured interviews. Feasibility will be assessed through task performance and health economic analysis. The latter will include quality-adjusted life years (QALY), which quantify quality and length of life, and modelling the Incremental Cost-Effectiveness Ratio (ICER), which compares the cost-effectiveness of the intervention based on cost per QALY gained. RESULTS We expect the robotic system to reduce caregiver time and associated costs, while enhancing autonomy, quality of life, and mental well-being. Potential technical and recruitment challenges have been identified and mitigation strategies planned. By evaluating real-life use of a WMRAs, this study may support the broader adoption of assistive robotic technologies. CONCLUSIONS This research offers key insights into the feasibility, usability, and economic value of robotic assistance for individuals with tetraplegia and will help inform future development and scale-up studies.

Commercially available wheelchair-mounted robotic arms (WMRAs) are becoming more prevalent internationally but have yet to be largely developed and approved by the Food and Drug Administration in the United States. The purpose of this study was to perform kinematic and experimental analysis of commercially available WMRAs. The goal was to quantitatively compare each device through a standardized testing protocol. The study and its procedures may serve as a source of quantitative evaluation data for commercially available and developmental WMRAs.

The goal of assistive robotic devices, such as a wheelchair-mounted robotic arms (WMRA), is to increase users’ functional independence. At odds with this goal is the fact that device interfaces tend to be rigid, requiring the user to adapt, rather than adapting to the user. Paperno, et al. (2016) identified key physical, cognitive, and sensory capabilities that affect an individual’s performance of simulated activities of daily living (e.g. picking up an object from the floor) while using a WMRA. Greater visual abilities (visual acuity, contrast sensitivity, and depth perception), cognitive abilities (processing speed, working memory, and spatial ability) and physical abilities (dexterity) resulted in participants completing tasks more quickly and with fewer total moves. We propose that interfaces should adapt to compensate for deficits in these capabilities to support a wider range of users. A variety of compensations should be developed and tested in order to identify the most effective techniques. For instance, object segmentation, a computer vision technique that separates objects and background in a visual scene, may compensate for deficits in contrast sensitivity, depth perception, processing speed, and working memories. However, contrast sensitivity may be better compensated for by use of a simple yellow filter on the screen, mimicking yellow lenses in glasses used for the same purpose. Similarly, depth perception limitations may be better overcome through the use of multiple camera views or by automating the pick-up and release mechanisms of the gripper. Thus there may be one compensation that facilitates WMRA use for a multitude of decrements or each factor may be better served by a specific separate compensation. In incorporating the effective compensations into the interface software, there should also be a capability of identifying which specific compensations should be activated for an individual user. For this we propose testing for these important individual differences should be included within the software. Virtual or online testing already exist for many of the identified factors and can be modified to fit our purpose. This is especially the case if gamification principles are applied as testing will engage user interest. In this way, the software can adjust compensations as a user’s visual, cognitive, and physical abilities change over time. Future research ventures will include identifying the most beneficial compensation for each identified individual difference and developing virtual gamified measures for those individual differences.

As the aging of the population becomes more severe, wheelchair-mounted robotic arms (WMRAs) are gaining an increased amount of attention. Laser pointer interactions are an attractive method enabling humans to unambiguously point out objects and pick them up. In addition, they bring about a greater sense of participation in the interaction process as an intuitive interaction mode. However, the issue of human–robot interactions remains to be properly tackled, and traditional laser point interactions still suffer from poor real-time performance and low accuracy amid dynamic backgrounds. In this study, combined with an advanced laser point detection method and an improved pose estimation algorithm, a laser pointer is used to facilitate the interactions between humans and a WMRA in an indoor environment. Assistive grasping using a laser selection consists of two key steps. In the first step, the images captured using an RGB-D camera are pre-processed, and then fed to a convolutional neural network (CNN) to determine the 2D coordinates of the laser point and objects within the image. Meanwhile, the centroid coordinates of the selected object are also obtained using the depth information. In this way, the object to be picked up and its location are determined. The experimental results show that the laser point can be detected with almost 100% accuracy in a complex environment. In the second step, a compound pose-estimation algorithm aiming at a sparse use of multi-view templates is applied, which consists of both coarse- and precise-matching of the target to the template objects, greatly improving the grasping performance. The proposed algorithms were implemented on a Kinova Jaco robotic arm, and the experimental results demonstrate their effectiveness. Compared with commonly accepted methods, the time consumption of the pose generation can be reduced from 5.36 to 4.43 s, and synchronously, the pose estimation error is significantly improved from 21.31% to 3.91%.

Wheelchair mounted robotic arms (WMRA) aim to help individuals with physical disabilities such as spine injuries or amputated arms by improving independent living and social participation of individuals with upper extremity impairments. Various research has been conducted and shown different ways of controlling the position of a robotic arm using Proportional Integral-derivative (PID). However, Fuzzy Logic Controller (FLC), when well designed, can behave like a nonlinear controller or even like a set of linear PID controllers that operate differently according to the inputs. Using FLC in this application has benefits in various aspects mentioned in the paper. Therefore, the focus of our research is to prove the performance of the studied position controller using Fuzzy Logic. The assessment is based on system efficiency, settling time to changes in the command signals and system overshoot.

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.