3-D-Gaze-Based Robotic Grasping Through Mimicking Human Visuomotor Function for People With Motion Impairments

Characterizing eye gaze and mental workload for assistive device control

Eye gaze tracking is increasingly popular due to improved technology and availability. In the domain of assistive device control, however, eye gaze tracking is often used in discrete ways (e.g., activating buttons on a screen), and does not harness the full potential of the gaze signal. In this article, we present a method for collecting both reactionary and controlled eye gaze signals, via screen-based tasks designed to isolate various types of eye movements. The resulting data allows us to build an individualized characterization for eye gaze interface use. Results from a study conducted with participants with motor impairments are presented, offering insights into maximizing the potential of eye gaze for assistive device control. Importantly, we demonstrate the potential for incorporating direct continuous eye gaze inputs into gaze-based interface designs; generally seen as intractable due to the 'Midas touch' problem of differentiating between gaze movements for perception versus for interface operation. Our key insight is to make use of an individualized measure of smooth pursuit characteristics to differentiate between gaze for control and gaze for environment scanning. We also present results relating to gaze-based metrics for mental workload and show the potential for the concurrent use of eye gaze for control input as well as assessing a user's mental workload both offline and in real-time. These findings might inform the development of continuous control paradigms using eye gaze, as well as the use of eye tracking as the sole input modality to systems that share control between human-generated and autonomy-generated inputs.

Development of a Virtual Robot Rehabilitation Training System for Children with Cerebral Palsy: An Observational Study

This paper presents the development of a robotic system for the rehabilitation and quality of life improvement of children with cerebral palsy (CP). The system consists of four modules and is based on a virtual humanoid robot that is meant to motivate and encourage children in their rehabilitation programs. The efficiency of the developed system was tested on two children with CP. The effect of using the robot is an increase in the number of exercise repetitions, as well as the time spent on therapy, developing and strengthening the child's musculature. Additionally, the children are able to produce socially acceptable gestures in the context of non-verbal communication for socialization. The main advantages of this system are its flexibility and ease of use. Besides the proposed use in CP rehabilitation, this system can be used in the rehabilitation of people recovering from surgery or injuries. Use of the proposed system significantly decreases the work load of the therapist who would be conducting the repetitive motion, allowing the therapist to see an increased number of patients. In the future, the number of different movements the robot is able to perform will be increased by way of domain-specific modelling and language.

Movement-Based Prosthesis Control with Angular Trajectory Is Getting Closer to Natural Arm Coordination

Traditional myoelectric controls of trans-humeral prostheses fail to provide intuitive coordination of the necessary degrees of freedom. We previously showed that by using artificial neural network predictions to reconstruct distal joints, based on the shoulder posture and movement goals (i.e., position and orientation of the targeted object), participants were able to position and orient an avatar hand to grasp objects with natural arm performances. However, this control involved rapid and unintended prosthesis movements at each modification of the movement goal, impractical for real-life scenarios. Here, we eliminate this abrupt change using novel methods based on an angular trajectory, determined from the speed of stump movement and the gap between the current and the 'goal' distal configurations. These new controls are tested offline and online (i.e., involving participants-in-the-loop) and compared to performances obtained with a natural control. Despite a slight increase in movement time, the new controls allowed twelve valid participants and six participants with trans-humeral limb loss to reach objects at various positions and orientations without prior training. Furthermore, no usability or workload degradation was perceived by participants with upper limb disabilities. The good performances achieved highlight the potential acceptability and effectiveness of those controls for our target population.

Empowering High-Level Spinal Cord Injury Patients in Daily Tasks With a Hybrid Gaze and FEMG-Controlled Assistive Robotic System

Individuals with high-level spinal cord injuries often face significant challenges in performing essential daily tasks due to their motor impairments. Consequently, the development of reliable, hands-free human-computer interfaces (HCI) for assistive devices is vital for enhancing their quality of life. However, existing methods, including eye-tracking and facial electromyogram (FEMG) control, have demonstrated limitations in stability and efficiency. To address these shortcomings, this paper presents an innovative hybrid control system that seamlessly integrates gaze and FEMG signals. When deployed as a hybrid HCI, this system has been successfully used to assist individuals with high-level spinal cord injuries in performing activities of daily living (ADLs), including tasks like eating, pouring water, and pick-and-place. Importantly, our experimental results confirm that our hybrid control method expedites the performance in pick-place tasks, achieving an average completion time of 34.3 s, which denotes a 28.8% and 21.8% improvement over pure gaze-based control and pure FEMG-based control, respectively. With practice, participants experienced up to a 44% efficiency improvement using the hybrid control method. This state-of-the-art system offers a highly precise and reliable intention interface, suitable for daily use by individuals with high-level spinal cord injuries, ultimately enhancing their quality of life and independence.

A scoping review of gaze and eye tracking-based control methods for assistive robotic arms

Background: Assistive Robotic Arms are designed to assist physically disabled people with daily activities. Existing joysticks and head controls are not applicable for severely disabled people such as people with Locked-in Syndrome. Therefore, eye tracking control is part of ongoing research. The related literature spans many disciplines, creating a heterogeneous field that makes it difficult to gain an overview. Objectives: This work focuses on ARAs that are controlled by gaze and eye movements. By answering the research questions, this paper provides details on the design of the systems, a comparison of input modalities, methods for measuring the performance of these controls, and an outlook on research areas that gained interest in recent years. Methods: This review was conducted as outlined in the PRISMA 2020 Statement. After identifying a wide range of approaches in use the authors decided to use the PRISMA-ScR extension for a scoping review to present the results. The identification process was carried out by screening three databases. After the screening process, a snowball search was conducted. Results: 39 articles and 6 reviews were included in this article. Characteristics related to the system and study design were extracted and presented divided into three groups based on the use of eye tracking. Conclusion: This paper aims to provide an overview for researchers new to the field by offering insight into eye tracking based robot controllers. We have identified open questions that need to be answered in order to provide people with severe motor function loss with systems that are highly useable and accessible.

Neural Correlation of EEG and Eye Movement in Natural Grasping Intention Estimation

Decoding the user's natural grasp intent enhances the application of wearable robots, improving the daily lives of individuals with disabilities. Electroencephalogram (EEG) and eye movements are two natural representations when users generate grasp intent in their minds, with current studies decoding human intent by fusing EEG and eye movement signals. However, the neural correlation between these two signals remains unclear. Thus, this paper aims to explore the consistency between EEG and eye movement in natural grasping intention estimation. Specifically, six grasp intent pairs are decoded by combining feature vectors and utilizing the optimal classifier. Extensive experimental results indicate that the coupling between the EEG and eye movements intent patterns remains intact when the user generates a natural grasp intent, and concurrently, the EEG pattern is consistent with the eye movements pattern across the task pairs. Moreover, the findings reveal a solid connection between EEG and eye movements even when taking into account cortical EEG (originating from the visual cortex or motor cortex) and the presence of a suboptimal classifier. Overall, this work uncovers the coupling correlation between EEG and eye movements and provides a reference for intention estimation.

Gaze and Environmental Context-Guided Deep Neural Network and Sequential Decision Fusion for Grasp Intention Recognition

Grasp intention recognition plays a crucial role in controlling assistive robots to aid older people and individuals with limited mobility in restoring arm and hand function. Among the various modalities used for intention recognition, the eye-gaze movement has emerged as a promising approach due to its simplicity, intuitiveness, and effectiveness. Existing gaze-based approaches insufficiently integrate gaze data with environmental context and underuse temporal information, leading to inadequate intention recognition performance. The objective of this study is to eliminate the proposed deficiency and establish a gaze-based framework for object detection and its associated intention recognition. A novel gaze-based grasp intention recognition and sequential decision fusion framework (GIRSDF) is proposed. The GIRSDF comprises three main components: gaze attention map generation, the Gaze-YOLO grasp intention recognition model, and sequential decision fusion models (HMM, LSTM, and GRU). To evaluate the performance of GIRSDF, a dataset named Invisible containing data from healthy individuals and hemiplegic patients is established. GIRSDF is validated by trial-based and subject-based experiments on Invisible and outperforms the previous gaze-based grasp intention recognition methods. In terms of running efficiency, the proposed framework can run at a frequency of about 22 Hz, which ensures real-time grasp intention recognition. This study is expected to inspire additional gaze-related grasp intention recognition works.

Characterizing Eye Gaze for Assistive Device Control

Eye gaze tracking is increasingly popular due to improved technology and availability. However, in assistive device control, eye gaze tracking is often limited to discrete control inputs. In this paper, we present a method for collecting both reactionary and control eye gaze signals to build an individualized characterization for eye gaze interface use. Results from a study conducted with motor-impaired participants are presented, offering insights into maximizing the potential of eye gaze for assistive device control. These findings can inform the development of continuous control paradigms using eye gaze.

Object Affordance-Based Implicit Interaction for Wheelchair-Mounted Robotic Arm Using a Laser Pointer

With the growth of the world's population, limited healthcare resources cannot provide adequate nursing services for all people in need. The wheelchair-mounted robotic arm (WMRA) with interactive technology could help to improve users' self-care ability and relieve nursing stress. However, the users struggle to control the WMRA due to complex operations. To use the WMRA with less burden, this paper proposes an object affordance-based implicit interaction technology using a laser pointer. Firstly, a laser semantic identification algorithm combined with the YOLOv4 and the support vector machine (SVM) is designed to identify laser semantics. Then, an implicit action intention reasoning algorithm, based on the concept of object affordance, is explored to infer users' intentions and learn their preferences. For the purpose of performing the actions about task intention in the scene, the dynamic movement primitives (DMP) and the finite state mechanism (FSM) are respectively used to generalize the trajectories of actions and reorder the sequence of actions in the template library. In the end, we verified the feasibility of the proposed technology on a WMRA platform. Compared with the previous method, the proposed technology can output the desired intention faster and significantly reduce the user's limb involvement time (about 85%) in operating the WMRA under the same task.

Non-Intrusive Real Time Eye Tracking Using Facial Alignment for Assistive Technologies

Most affordable eye tracking systems use either intrusive setup such as head-mounted cameras or use fixed cameras with infrared corneal reflections via illuminators. In the case of assistive technologies, using intrusive eye tracking systems can be a burden to wear for extended periods of time and infrared based solutions generally do not work in all environments, especially outside or inside if the sunlight reaches the space. Therefore, we propose an eye-tracking solution using state-of-the-art convolutional neural network face alignment algorithms that is both accurate and lightweight for assistive tasks such as selecting an object for use with assistive robotics arms. This solution uses a simple webcam for gaze and face position and pose estimation. We achieve a much faster computation time than the current state-of-the-art while maintaining comparable accuracy. This paves the way for accurate appearance-based gaze estimation even on mobile devices, giving an average error of around 4.5° on the MPIIGaze dataset (Zhang et al., 2019) and state-of-the-art average errors of 3.9° and 3.3° on the UTMultiview (Sugano et al., 2014) and GazeCapture (Krafka et al., 2016; Park et al., 2019) datasets respectively, while achieving a decrease in computation time of up to 91%.

User Based Development and Test of the EXOTIC Exoskeleton: Empowering Individuals with Tetraplegia Using a Compact, Versatile, 5-DoF Upper Limb Exoskeleton Controlled through Intelligent Semi-Automated Shared Tongue Control

This paper presents the EXOTIC- a novel assistive upper limb exoskeleton for individuals with complete functional tetraplegia that provides an unprecedented level of versatility and control. The current literature on exoskeletons mainly focuses on the basic technical aspects of exoskeleton design and control while the context in which these exoskeletons should function is less or not prioritized even though it poses important technical requirements. We considered all sources of design requirements, from the basic technical functions to the real-world practical application. The EXOTIC features: (1) a compact, safe, wheelchair-mountable, easy to don and doff exoskeleton capable of facilitating multiple highly desired activities of daily living for individuals with tetraplegia; (2) a semi-automated computer vision guidance system that can be enabled by the user when relevant; (3) a tongue control interface allowing for full, volitional, and continuous control over all possible motions of the exoskeleton. The EXOTIC was tested on ten able-bodied individuals and three users with tetraplegia caused by spinal cord injury. During the tests the EXOTIC succeeded in fully assisting tasks such as drinking and picking up snacks, even for users with complete functional tetraplegia and the need for a ventilator. The users confirmed the usability of the EXOTIC.

High-Accuracy 3D Gaze Estimation with Efficient Recalibration for Head-Mounted Gaze Tracking Systems

The problem of 3D gaze estimation can be viewed as inferring the visual axes from eye images. It remains a challenge especially for the head-mounted gaze tracker (HMGT) with a simple camera setup due to the complexity of the human visual system. Although the mainstream regression-based methods could establish the mapping relationship between eye image features and the gaze point to calculate the visual axes, it may lead to inadequate fitting performance and appreciable extrapolation errors. Moreover, regression-based methods suffer from a degraded user experience because of the increased burden in recalibration procedures when slippage occurs between HMGT and head. To address these issues, a high-accuracy 3D gaze estimation method along with an efficient recalibration approach is proposed with head pose tracking in this paper. The two key parameters, eyeball center and camera optical center, are estimated in head frame with geometry-based method, so that a mapping relationship between two direction features is proposed to calculate the direction of the visual axis. As the direction features are formulated with the accurately estimated parameters, the complexity of mapping relationship could be reduced and a better fitting performance can be achieved. To prevent the noticeable extrapolation errors, direction features with uniform angular intervals for fitting the mapping are retrieved over human’s field of view. Additionally, an efficient single-point recalibration method is proposed with an updated eyeball coordinate system, which reduces the burden of calibration procedures significantly. Our experiment results show that the calibration and recalibration methods could improve the gaze estimation accuracy by 35 percent (from a mean error of 2.00 degrees to 1.31 degrees) and 30 percent (from a mean error of 2.00 degrees to 1.41 degrees), respectively, compared with the state-of-the-art methods.

Face-Computer Interface (FCI): Intent Recognition Based on Facial Electromyography (fEMG) and Online Human-Computer Interface With Audiovisual Feedback

Patients who have lost limb control ability, such as upper limb amputation and high paraplegia, are usually unable to take care of themselves. Establishing a natural, stable, and comfortable human-computer interface (HCI) for controlling rehabilitation assistance robots and other controllable equipments will solve a lot of their troubles. In this study, a complete limbs-free face-computer interface (FCI) framework based on facial electromyography (fEMG) including offline analysis and online control of mechanical equipments was proposed. Six facial movements related to eyebrows, eyes, and mouth were used in this FCI. In the offline stage, 12 models, eight types of features, and three different feature combination methods for model inputing were studied and compared in detail. In the online stage, four well-designed sessions were introduced to control a robotic arm to complete drinking water task in three ways (by touch screen, by fEMG with and without audio feedback) for verification and performance comparison of proposed FCI framework. Three features and one model with an average offline recognition accuracy of 95.3%, a maximum of 98.8%, and a minimum of 91.4% were selected for use in online scenarios. In contrast, the way with audio feedback performed better than that without audio feedback. All subjects completed the drinking task in a few minutes with FCI. The average and smallest time difference between touch screen and fEMG under audio feedback were only 1.24 and 0.37 min, respectively.

GazeEMD: Detecting Visual Intention in Gaze-Based Human-Robot Interaction

In gaze-based Human-Robot Interaction (HRI), it is important to determine human visual intention for interacting with robots. One typical HRI interaction scenario is that a human selects an object by gaze and a robotic manipulator will pick up the object. In this work, we propose an approach, GazeEMD, that can be used to detect whether a human is looking at an object for HRI application. We use Earth Mover’s Distance (EMD) to measure the similarity between the hypothetical gazes at objects and the actual gazes. Then, the similarity score is used to determine if the human visual intention is on the object. We compare our approach with a fixation-based method and HitScan with a run length in the scenario of selecting daily objects by gaze. Our experimental results indicate that the GazeEMD approach has higher accuracy and is more robust to noises than the other approaches. Hence, the users can lessen cognitive load by using our approach in the real-world HRI scenario.

Gaze-Based Intention Estimation for Shared Autonomy in Pick-and-Place Tasks

Shared autonomy aims at combining robotic and human control in the execution of remote, teleoperated tasks. This cooperative interaction cannot be brought about without the robot first recognizing the current human intention in a fast and reliable way so that a suitable assisting plan can be quickly instantiated and executed. Eye movements have long been known to be highly predictive of the cognitive agenda unfolding during manual tasks and constitute, hence, the earliest and most reliable behavioral cues for intention estimation. In this study, we present an experiment aimed at analyzing human behavior in simple teleoperated pick-and-place tasks in a simulated scenario and at devising a suitable model for early estimation of the current proximal intention. We show that scan paths are, as expected, heavily shaped by the current intention and that two types of Gaussian Hidden Markov Models, one more scene-specific and one more action-specific, achieve a very good prediction performance, while also generalizing to new users and spatial arrangements. We finally discuss how behavioral and model results suggest that eye movements reflect to some extent the invariance and generality of higher-level planning across object configurations, which can be leveraged by cooperative robotic systems.

Towards Robust Robot Control in Cartesian Space Using an Infrastructureless Head- and Eye-Gaze Interface

This paper presents a lightweight, infrastructureless head-worn interface for robust and real-time robot control in Cartesian space using head- and eye-gaze. The interface comes at a total weight of just 162 g. It combines a state-of-the-art visual simultaneous localization and mapping algorithm (ORB-SLAM 2) for RGB-D cameras with a Magnetic Angular rate Gravity (MARG)-sensor filter. The data fusion process is designed to dynamically switch between magnetic, inertial and visual heading sources to enable robust orientation estimation under various disturbances, e.g., magnetic disturbances or degraded visual sensor data. The interface furthermore delivers accurate eye- and head-gaze vectors to enable precise robot end effector (EFF) positioning and employs a head motion mapping technique to effectively control the robots end effector orientation. An experimental proof of concept demonstrates that the proposed interface and its data fusion process generate reliable and robust pose estimation. The three-dimensional head- and eye-gaze position estimation pipeline delivers a mean Euclidean error of 19.0±15.7 mm for head-gaze and 27.4±21.8 mm for eye-gaze at a distance of 0.3–1.1 m to the user. This indicates that the proposed interface offers a precise control mechanism for hands-free and full six degree of freedom (DoF) robot teleoperation in Cartesian space by head- or eye-gaze and head motion.

Task-Space Synergies for Reaching Using Upper-Limb Prostheses

Synergistic prostheses enable the coordinated movement of the human-prosthetic arm, as required by activities of daily living. This is achieved by coupling the motion of the prosthesis to the human command, such as the residual limb movement in motion-based interfaces. Previous studies demonstrated that developing human-prosthetic synergies in joint-space must consider individual motor behaviour and the intended task to be performed, requiring personalisation and task calibration. In this work, an alternative synergy-based strategy, utilising a synergistic relationship expressed in task-space, is proposed. This task-space synergy has the potential to replace the need for personalisation and task calibration with a model-based approach requiring knowledge of the individual user's arm kinematics, the anticipated hand motion during the task and voluntary information from the prosthetic user. The proposed method is compared with surface electromyography-based and joint-space synergy-based prosthetic interfaces in a study of motor behaviour and task performance on able-bodied subjects using a VR-based transhumeral prosthesis. Experimental results showed that for a set of forward reaching tasks the proposed task-space synergy achieves comparable performance to joint-space synergies without the need to rely on time-consuming calibration processes or human motor learning. Case study results with an amputee subject motivate the further development of the proposed task-space synergy method.

Toward Shared Autonomy Control Schemes for Human-Robot Systems: Action Primitive Recognition Using Eye Gaze Features

The functional independence of individuals with upper limb impairment could be enhanced by teleoperated robots that can assist with activities of daily living. However, robot control is not always intuitive for the operator. In this work, eye gaze was leveraged as a natural way to infer human intent and advance action recognition for shared autonomy control schemes. We introduced a classifier structure for recognizing low-level action primitives that incorporates novel three-dimensional gaze-related features. We defined an action primitive as a triplet comprised of a verb, target object, and hand object. A recurrent neural network was trained to recognize a verb and target object, and was tested on three different activities. For a representative activity (making a powdered drink), the average recognition accuracy was 77% for the verb and 83% for the target object. Using a non-specific approach to classifying and indexing objects in the workspace, we observed a modest level of generalizability of the action primitive classifier across activities, including those for which the classifier was not trained. The novel input features of gaze object angle and its rate of change were especially useful for accurately recognizing action primitives and reducing the observational latency of the classifier.

Semi-Autonomous Robotic Arm Reaching With Hybrid Gaze-Brain Machine Interface

Recent developments in the non-muscular human-robot interface (HRI) and shared control strategies have shown potential for controlling the assistive robotic arm by people with no residual movement or muscular activity in upper limbs. However, most non-muscular HRIs only produce discrete-valued commands, resulting in non-intuitive and less effective control of the dexterous assistive robotic arm. Furthermore, the user commands and the robot autonomy commands usually switch in the shared control strategies of such applications. This characteristic has been found to yield a reduced sense of agency as well as frustration for the user according to previous user studies. In this study, we firstly propose an intuitive and easy-to-learn-and-use hybrid HRI by combing the Brain-machine interface (BMI) and the gaze-tracking interface. For the proposed hybrid gaze-BMI, the continuous modulation of the movement speed via the motor intention occurs seamlessly and simultaneously to the unconstrained movement direction control with the gaze signals. We then propose a shared control paradigm that always combines user input and the autonomy with the dynamic combination regulation. The proposed hybrid gaze-BMI and shared control paradigm were validated for a robotic arm reaching task performed with healthy subjects. All the users were able to employ the hybrid gaze-BMI for moving the end-effector sequentially to reach the target across the horizontal plane while also avoiding collisions with obstacles. The shared control paradigm maintained as much volitional control as possible, while providing the assistance for the most difficult parts of the task. The presented semi-autonomous robotic system yielded continuous, smooth, and collision-free motion trajectories for the end effector approaching the target. Compared to a system without assistances from robot autonomy, it significantly reduces the rate of failure as well as the time and effort spent by the user to complete the tasks.

System Transparency in Shared Autonomy: A Mini Review

What does transparency mean in a shared autonomy framework? Different ways of understanding system transparency in human-robot interaction can be found in the state of the art. In one of the most common interpretations of the term, transparency is the observability and predictability of the system behavior, the understanding of what the system is doing, why, and what it will do next. Since the main methods to improve this kind of transparency are based on interface design and training, transparency is usually considered a property of such interfaces, while natural language explanations are a popular way to achieve transparent interfaces. Mechanical transparency is the robot capacity to follow human movements without human-perceptible resistive forces. Transparency improves system performance, helping reduce human errors, and builds trust in the system. One of the principles of user-centered design is to keep the user aware of the state of the system: a transparent design is a user-centered design. This article presents a review of the definitions and methods to improve transparency for applications with different interaction requirements and autonomy degrees, in order to clarify the role of transparency in shared autonomy, as well as to identify research gaps and potential future developments.

A Novel Method for Estimating Free Space 3D Point-of-Regard Using Pupillary Reflex and Line-of-Sight Convergence Points

In this paper, a novel 3D gaze estimation method for a wearable gaze tracking device is proposed. This method is based on the pupillary accommodation reflex of human vision. Firstly, a 3D gaze measurement model is built. By uniting the line-of-sight convergence point and the size of the pupil, this model can be used to measure the 3D Point-of-Regard in free space. Secondly, a gaze tracking device is described. By using four cameras and semi-transparent mirrors, the gaze tracking device can accurately extract the spatial coordinates of the pupil and eye corner of the human eye from images. Thirdly, a simple calibration process of the measuring system is proposed. This method can be sketched as follows: (1) each eye is imaged by a pair of binocular stereo cameras, and the setting of semi-transparent mirrors can support a better field of view; (2) the spatial coordinates of the pupil center and the inner corner of the eye in the images of the stereo cameras are extracted, and the pupil size is calculated with the features of the gaze estimation method; (3) the pupil size and the line-of-sight convergence point when watching the calibration target at different distances are computed, and the parameters of the gaze estimation model are determined. Fourthly, an algorithm for searching the line-of-sight convergence point is proposed, and the 3D Point-of-Regard is estimated by using the obtained line-of-sight measurement model. Three groups of experiments were conducted to prove the effectiveness of the proposed method. This approach enables people to obtain the spatial coordinates of the Point-of-Regard in free space, which has great potential in the application of wearable devices.

Exploiting Three-Dimensional Gaze Tracking for Action Recognition During Bimanual Manipulation to Enhance Human-Robot Collaboration

Human-robot collaboration could be advanced by facilitating the intuitive, gaze-based control of robots, and enabling robots to recognize human actions, infer human intent, and plan actions that support human goals. Traditionally, gaze tracking approaches to action recognition have relied upon computer vision-based analyses of two-dimensional egocentric camera videos. The objective of this study was to identify useful features that can be extracted from three-dimensional (3D) gaze behavior and used as inputs to machine learning algorithms for human action recognition. We investigated human gaze behavior and gaze-object interactions in 3D during the performance of a bimanual, instrumental activity of daily living: the preparation of a powdered drink. A marker-based motion capture system and binocular eye tracker were used to reconstruct 3D gaze vectors and their intersection with 3D point clouds of objects being manipulated. Statistical analyses of gaze fixation duration and saccade size suggested that some actions (pouring and stirring) may require more visual attention than other actions (reach, pick up, set down, and move). 3D gaze saliency maps, generated with high spatial resolution for six subtasks, appeared to encode action-relevant information. The "gaze object sequence" was used to capture information about the identity of objects in concert with the temporal sequence in which the objects were visually regarded. Dynamic time warping barycentric averaging was used to create a population-based set of characteristic gaze object sequences that accounted for intra- and inter-subject variability. The gaze object sequence was used to demonstrate the feasibility of a simple action recognition algorithm that utilized a dynamic time warping Euclidean distance metric. Averaged over the six subtasks, the action recognition algorithm yielded an accuracy of 96.4%, precision of 89.5%, and recall of 89.2%. This level of performance suggests that the gaze object sequence is a promising feature for action recognition whose impact could be enhanced through the use of sophisticated machine learning classifiers and algorithmic improvements for real-time implementation. Robots capable of robust, real-time recognition of human actions during manipulation tasks could be used to improve quality of life in the home and quality of work in industrial environments.