Eating unaided remains impossible for millions of people with upper-limb impairments, turning every meal into a reminder of lost autonomy. Demographic change will magnify this challenge. By 2050, 1 in 6 individuals will be aged over 65 years, up from 1 in 11 in 2024 [1]. Aging correlates with disability, and about 1 billion people already live with an impairment, a figure set to rise as populations gray [2]. The result is a rapidly growing cohort that needs daily feeding assistance, further straining care networks that are already under pressure [1,3]. Assistive devices are critical, especially in low-resource settings where professional carers are limited. In the United Kingdom, for example, many adults aged over 75 years live alone and receive only brief daily visits, leading to malnutrition and declining health [4,5].

Robotic feeding assistants offer a promising solution, demonstrating consistent patience, adaptability, and precision. By tailoring actions to user preferences, they can provide a personalized and empowering dining experience [6,7].

Several commercial and noncommercial assistive feeding robots have already been developed to support individuals with upper limb impairments [8-11]. Examples include My Spoon (SECOM Co, Ltd) [12], SnackBot (Carnegie Mellon University Robotics Institute) [13], the Assistive Robotic Manipulator (Exact Dynamics Ltd) [14], Obi Robot (DESiN LLC) [15], iEat (Assistive Innovations) [16], and Bestic (Camanio Care AB) [17]. Table 1 outlines the key advantages, limitations, and available quantitative evidence for 7 commercial or research-based assistive feeding systems. While some platforms offer user-friendly features such as switch-based control or teach modes, most lack real-time adaptation, user-state sensing, and robust bite-delivery evaluation. The table highlights the current gaps in autonomy, sensor integration, and evidence-based validation across devices. These are predominantly passive, relying on fixed routines with limited adaptability [18]. Therefore, they struggle with delayed user responses, varied food types, and environmental challenges such as poor lighting or plate movement.

Various researchers have aimed to improve adaptability and intelligence through sensing and control strategies. Predictive models such as hidden Markov models have been used to estimate bite timing based on social cues and food characteristics, achieving timing errors of 1.57 seconds [7]. However, these systems often operate in open-loop and cannot adjust if the user hesitates. Similarly, support vector machines and convolutional neural networks (CNNs) [21,22] have been employed to locate bites on a plate, but they also lack real-time feedback.

More advanced systems have integrated vision and control frameworks. For instance, Mashrur et al [23] used a Personal Robot 2 (PR2; Willow Garage) equipped with faster region-based CNN and red green blue–depth (RGB-D) cameras, achieving 93% precision and an 82.8% food delivery success rate. A study by Park et al [8] deployed PR2 with a GUI interface and model predictive controller, enhancing arm safety and anomaly detection, though food handling precision remained a challenge. Hybrid controllers, such as an adaptive neuro-fuzzy inference system–proportional integral derivative controller [24] and fuzzy logic-based inverse kinematics (IKs) [25], have improved control smoothness and efficiency, but they lack real-time mouth-state feedback.

Artificial neural networks have also been applied to solve IKs [26], and low-cost platforms, such as 3D-printed arms with facial recognition [27], have broadened accessibility. However, these systems continue to rely on scripted motions. Studies conducted by Serrezuela et al [28], Mystkowski et al [29], and Gilca [30] demonstrate promise in trajectory tracking and facial landmark detection; however, sensor fusion and robustness under environmental variation remain limited. Simulation tools [31] and digital twins [32] have explored virtual training and control evaluation, but fall short of real-world responsiveness. Together, these limitations highlight the need for a fully adaptive, closed-loop feeding system that anticipates user intent, verifies safe delivery, and dynamically adjusts in real-time. To meet this need, we present a novel autonomous feeding robot that integrates time-series forecasting, machine learning, and multisensor feedback to deliver personalized, safe, and efficient feeding assistance. Our system combines autoregressive integrated moving average (ARIMA) models (to capture linear trends) with a feed-forward neural network (FFNN; to learn nonlinear behaviors), enabling real-time predictions of mouth readiness. Data from vision-based mouth detection, ultrasonic distance sensing, and strain-gauge force feedback closes the loop, allowing the robot to respond to delayed reactions, variable food textures, lighting changes, and plate movement. This study advances assistive feeding robotics through five key contributions:

After powering on the robot, an initialization subroutine begins, using stored positioning memory, ARIMA optimizations, and FFNN refinements. At each step during the robot’s operation, sensory data were used as training data for the FFNN and ARIMA models to enhance the patient experience further. The trained neural network was then used to initialize subsequent robot startup sequences. Figure 3A shows the front view of the primary camera in Unity, and Figure 3B highlights the facial landmarks and distances between points on the face to identify whether the mouth is open or closed. Figure 3C presents 2 perspectives: one from the primary camera and the other from the feeder camera, illustrating the integration of the object detection algorithm.

The combined ARIMA and FFNN models predict the next system state to optimize portion size and timing while also handling sensor-based adjustments. The strain gauge adjusts pressure for stabbing or scooping food, the sonar sensor adjusts position and timing based on user proximity, and the camera ensures the robotic arm only operates when the user’s mouth is open. This real-time camera feedback enhances safety and efficiency by adjusting feeding dynamics based on sensor inputs, optimizing the overall feeding process. Recurring use of the robot by the same patient can improve response time, reduce delays in sequence execution, and smooth the robot’s kinematics.

The performance of the feeding robot was evaluated across 4 simulated scenarios, with a focus on success rates, timing accuracy, and force application. The combined ARIMA and FFNN models demonstrated significant improvements in feeding accuracy and personalization. Object detection, adaptive learning, and statistical validations were conducted to illustrate system robustness.

Table 2 summarizes the robot’s performance metrics. Scenario 1 achieved the highest success rate (95%) and the lowest response time (1.5 s), while scenario 4 demonstrated adaptability to dynamic environments with a success rate of 80%. ANOVA revealed significant differences in success rates across scenarios (F_3,146=12.34; P=.002), with scenario 1 (mean success rate of 95%, SD approximately 3.2%) outperforming scenario 3 (mean success rate of 85%, SD approximately 4.1%; P=.030) and scenario 4 (mean success rate of 80%, SD approximately 5.0%; P=.010).

The four scenarios simulated in Unity to test the control algorithms for the assistive feeding robot are as follows:

Table 3 presents the object detection metrics of the system, including precision, recall, and confidence, across key tasks. For example, the plate detection precision was 95%, while the face detection precision reached 97%. These metrics ensure accurate feeding operations, enhancing user safety and satisfaction.

The combined ARIMA and FFNN model significantly outperformed standalone ARIMA and FFNN models. It achieved the lowest MSE (0.008) compared with 0.015 and 0.012, respectively, and the highest R² (94%), relative to 85% and 88%, respectively. These results indicate the superior capability of the hybrid model to capture user interaction dynamics.

A fuzzy logic system was used to regulate the force and angle for food forking. Inputs included plate distance (ultrasonic), fruit type (camera classification), and resistance (load cell). Rules were defined for different fruit types:

Force and angle adjustments were verified by comparing expected versus measured values recorded by sensors during the operation.

Figure 4 illustrates the robotic arm in a real-world setting while performing food targeting, picking, and delivery tasks. In Figure 4A-B, the apple is successfully forked and picked up, showcasing the effectiveness of the stabbing mechanism. Finally, Figure 4C shows the robotic arm delivering the food to a designated mouth position. This demonstrates the robot’s operational workflow, covering critical steps of targeting, stabbing, and delivering food to a target. The experiment under scenario one yielded an average accuracy rate of 87% over 50 iterations, emphasizing the system’s reliability and consistency.

Figure 5 illustrates the iterative improvements in success rate and response time over 150 iterations in the fourth scenario of the simulated environment. Key parameters analyzed included stabbing angles, applied forces, and success rates for apples, demonstrating the robot’s adaptability to varying conditions. It showed a steady improvement in success rate, starting at approximately 40% in initial iterations and stabilizing at 90% after 90 iterations. This progression underscores the effectiveness of the adaptive learning approach in optimizing performance. Figure 5 also illustrates the corresponding reduction in response time, which starts at 3.6 seconds and improves to 2.2 seconds (P<.001), highlighting enhanced efficiency.

The kinematic response and stability of the robot have also been enhanced, as demonstrated in Figure 6. Figure 6A illustrates the angular movement of each joint during a specific motion sequence. In contrast, Figure 6 depicts the 3D spatial positioning of the actuator during the execution of the same movement set. Both graphs compare the optimized system’s performance to the original untrained control system output. Notable improvements in the robot’s pathfinding and control system output are evident in both graphs. In Figure 6A, there is an apparent reduction in the overshooting and undershooting of servo adjustments needed to achieve the desired joint angles, minimizing errors and enhancing precision. These refinements have significantly reduced the jerkiness in the robot’s arm movements and diminished vibrations in the final actuator. Consequently, food delivery to the user is smoother and more controlled, which underscores the improved kinematic performance and stability of the robot.

Figure 7 illustrates the facial recognition and mouth detection neural networks integrated into the robot. Using advanced image recognition techniques. Using a VGG 16 convolutional network (proposed by the Visual Geometry Group at the University of Oxford [33]), the robot detects and highlights the users’ facial features. It determines if they have an open mouth or not before approaching the user. The neural network initially separates the user’s face from the background and then identifies the control volume box where the user’s mouth is located. Depending on the facial characteristics of a user, the neural network constantly updates the status of the user between mouth open and closed. This is a key feature of the robot that enables it to perform the feeding task reliably, providing a layer of system control within the feeding sequence. Whenever the user has a closed mouth, the feeding sequence is halted, waiting for either an override command or the mouth open status to continue the feeding sequence.