Publications
12 results found
Spiers AJ, Young E, Kuchenbecker KJ, 2022, The S-BAN: insights into the perception of shape-changing haptic interfaces via virtual pedestrian navigation, ACM Transactions on Computer-Human Interaction, ISSN: 1073-0516
Screen-based pedestrian navigation assistance can be distracting or inaccessible to users. Shape-changing haptic interfaces can overcome these concerns. The S-BAN is a new handheld haptic interface that utilizes a parallel kinematic structure to deliver 2-DOF spatial information over a continuous workspace, with a form factor suited to integration with other travel aids. The ability to pivot, extend and retract its body opens possibilities and questions around spatial data representation. We present a static study to understand user perception of absolute pose and relative motion for two spatial mappings, showing highest sensitivity to relative motions in the cardinal directions. We then present an embodied navigation experiment in virtual reality. User motion efficiency when guided by the S-BAN was statistically equivalent to using a vision-based tool (a smartphone proxy). Although haptic trials were slower than visual trials, participants’ heads were more elevated with the S-BAN, allowing greater visual focus on the environment.
Gueorguiev D, Javot B, Spiers A, et al., 2022, Larger Skin-Surface Contact Through a Fingertip Wearable Improves Roughness Perception, 13th International Conference on Human Haptic Sensing and Touch Enabled Computer Applications (EuroHaptics), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 171-179, ISSN: 0302-9743
Spiers A, Cochran J, Resnik L, et al., 2021, Quantifying prosthetic and intact limb use in upper limb amputees via egocentric video: an unsupervised, at-home study, IEEE Transactions on Medical Robotics and Bionics, Vol: 3, Pages: 463-484, ISSN: 2576-3202
Analysis of the manipulation strategies employed by upper-limb prosthetic device users can provide valuable insights into the shortcomings of current prosthetic technology or therapeutic interventions. Typically, this problem has been approached with survey or lab-based studies, whose prehensile-grasp-focused results do not necessarily give accurate representations of daily activity. In this work, we capture prosthesis-user behavior in the unstructured and familiar environments of the participants own homes. Compact head-mounted video cameras recorded ego-centric views of the hands during self-selected household chores. Over 60 hours of video was recorded from 8 persons with unilateral amputation or limb difference (6 transradial, 1 transhumeral, 1 shoulder). Of this, almost 16 hours of video data was analyzed by human experts using the 22-category ‘TULIP’ custom manipulation taxonomy, producing the type and duration of over 27,000 prehensile and non-prehensile manipulation tags on both upper limbs, permitting a level of objective analysis not previously possible with this population. Our analysis included unique observations on non-prehensile manipulations occurrence, determining that 79% of transradial body-powered device manipulations were non-prehensile, compared to 60% for transradial myoelectric devices. Conversely, only 16-19% of intact limb activity was non-prehensile. Additionally, multi-grasp terminal devices did not lead to increased activity compared to 1DOF devices.
Sahin A, Spiers AJ, Calli B, 2021, Region-Based Planning for 3D Within-Hand-Manipulation via Variable Friction Robot Fingers and Extrinsic Contacts, IEEE International Conference on Robotics and Automation (ICRA), Publisher: IEEE, Pages: 6549-6555, ISSN: 1050-4729
Gloumakov Y, Spiers AJ, Dollar AM, 2020, Dimensionality reduction and motion clustering during activities of daily living: decoupling hand location and orientation, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol: 28, Pages: 2955-2965, ISSN: 1534-4320
This article is the second in a two-part series analyzing human arm and hand motion during a wide range of unstructured tasks. In this work, we track the hand of healthy individuals as they perform a variety of activities of daily living (ADLs) in three ways decoupled from hand orientation: end-point locations of the hand trajectory, whole path trajectories of the hand, and straight-line paths generated using start and end points of the hand. These data are examined by a clustering procedure to reduce the wide range of hand use to a smaller representative set. Hand orientations are subsequently analyzed for the end-point location clustering results and subsets of orientations are identified in three reference frames: global, torso, and forearm. Data driven methods that are used include dynamic time warping (DTW), DTW barycenter averaging (DBA), and agglomerative hierarchical clustering with Ward’s linkage. Analysis of the end-point locations, path trajectory, and straight-line path trajectory identified 5, 5, and 7 ADL task categories, respectively, while hand orientation analysis identified up to 4 subsets of orientations for each task location, discretized and classified to the facets of a rhombicuboctahedron. Together these provide insight into our hand usage in daily life and inform an implementation in prosthetic or robotic devices using sequential control.
Gloumakov Y, Spiers AJ, Dollar AM, 2020, Dimensionality reduction and motion clustering during activities of daily living: three-, four-, and seven-degree-of-freedom arm movements, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol: 28, Pages: 2826-2836, ISSN: 1534-4320
This paper is the first in a two-part series analyzing human arm and hand motion during a wide range of unstructured tasks. The wide variety of motions performed by the human arm during daily tasks makes it desirable to find representative subsets to reduce the dimensionality of these movements for a variety of applications, including the design and control of robotic and prosthetic devices. This paper presents a novel method and the results of an extensive human subjects study to obtain representative arm joint angle trajectories that span naturalistic motions during Activities of Daily Living (ADLs). In particular, we seek to identify sets of useful motion trajectories of the upper limb that are functions of a single variable, allowing, for instance, an entire prosthetic or robotic arm to be controlled with a single input from a user, along with a means to select between motions for different tasks. Data driven approaches are used to discover clusters and representative motion averages for the wrist 3 degree of freedom (DOF), elbow-wrist 4 DOF, and full-arm 7 DOF motions. The proposed method makes use of well-known techniques such as dynamic time warping (DTW) to obtain a divergence measure between motion segments, Ward’s distance criterion to build hierarchical trees, and functional principal component analysis (fPCA) to evaluate cluster variability. The emerging clusters associate various recorded motions into primarily hand start and end location for the full-arm system, motion direction for the wrist-only system, and an intermediate between the two qualities for the elbow-wrist system.
Spiers AJ, Morgan AS, Srinivasan K, et al., 2020, Using a variable-friction robot hand to determine proprioceptive features for object classification during within-hand-manipulation, IEEE Transactions on Haptics, Vol: 13, Pages: 600-610, ISSN: 1939-1412
Interactions with an object during within-hand manipulation (WIHM) constitutes an assortment of gripping, sliding, and pivoting actions. In addition to manipulation benefits, the re-orientation and motion of the objects within-the-hand also provides a rich array of additional haptic information via the interactions to the sensory organs of the hand. In this article, we utilize variable friction (VF) robotic fingers to execute a rolling WIHM on a variety of objects, while recording `proprioceptive' actuator data, which is then used for object classification (i.e., without tactile sensors). Rather than hand-picking a select group of features for this task, our approach begins with 66 general features, which are computed from actuator position and load profiles for each object-rolling manipulation, based on gradient changes. An Extra Trees classifier performs object classification while also ranking each feature's importance. Using only the six most-important `Key Features' from the general set, a classification accuracy of 86% was achieved for distinguishing the six geometric objects included in our data set. Comparatively, when all 66 features are used, the accuracy is 89.8%.
Spiers AJ, Gloumakov Y, Dollar AM, 2018, Examining the impact of wrist mobility on reaching motion compensation across a discretely sampled workspace, 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob), Publisher: IEEE, Pages: 819-826
This paper presents an effort to characterize the impact of wrist mobility on reaching motion compensation over an evenly sampled planar workspace. When the degrees of freedom of the arm are limited due to injury or amputation, the behavior of other joints is modified to achieve the same motion goals. Though several past studies have measured motion compensation for simulated activities of daily living, the results tend to be specific to one spatial configuration of user and objects. Conversely, this paper aims to understand how motions and compensation vary when the same task (reaching-and-grasping) is conducted at a variety of locations across in the workspace. This high-resolution sampling enables spatial patterns of unimpaired and impaired movement to be identified. To achieve this, joint angles and Cartesian trajectories of the upper body were recorded as able-bodied participants reached and grasped 49 (7×7) equally spaced vertical cylindrical targets on a 1.9xl.9m grid, using their dominant hand. This was first completed naturally and then while wearing a custom orthopedic arm brace, which limits all 3DOF (degrees of freedom) of wrist motion. Each reaching motion was segmented and independently analyzed using metrics for range of motion, and Cartesian path length of body segments. A spatial `heat-map' display approach visually indicates how regions of the workspace affect the behavior of different body joints and segments. Further statistical analysis quantifies these visual trends. The results indicate wrist mobility has significant impact on shoulder and elbow ROM in addition to the length of Cartesian motion trajectories for the wrist and elbow.
Spiers AJ, Calli B, Dollar AM, 2018, Variable-friction finger surfaces to enable within-hand manipulation via gripping and sliding, IEEE Robotics and Automation Letters, Vol: 3, Pages: 4116-4123, ISSN: 2377-3766
The human hand is able to achieve an unparalleled diversity of manipulation actions. One contributor to this capability is the structure of the human finger pad, where soft internal tissue is surrounded by a layer of more rigid skin. This permits conforming of the finger pad around object contours for firm grasping, while also permitting low-friction sliding over object surfaces with a light touch. These varying modes of manipulation contribute to the common ability for in-hand-manipulation, where an object (such as a car key) may repositioned relative to the palm. In this letter, we present a simple mechanical analogy to the human finger pad, via a robotic finger with both high- and low-friction surfaces. The low-friction surface is suspended on elastic elements and recesses into a cavity when a sufficient normal force is applied (~1.2 to 2.5 N depending on contact location), exposing the high-friction surface. We implement one “variable friction” finger and one “constant friction” finger on a 2-DOF gripper with a simple torque controller. With this setup, we demonstrate how within-hand rolling and sliding of an object may be achieved without the need for tactile sensing, high-dexterity, dynamic finger/object modeling, or complex control methods. The addition of an actuator to the finger design allows controlled switching between variable-friction and constant-friction modes, enabling precise object translation and reorientation within a grasp, via simple motion sequences. The rolling and sliding behaviors are characterized with experimentally verified geometric models.
Spiers AJ, Resnik L, Dollar AM, 2017, Analyzing at-home prosthesis use in unilateral upper-limb amputees to inform treatment & device design, 2017 International Conference on Rehabilitation Robotics (ICORR), Publisher: IEEE
Spiers AJ, Dollar AM, 2017, Design and evaluation of shape-changing haptic interfaces for pedestrian navigation assistance, IEEE Transactions on Haptics, Vol: 10, Pages: 17-28, ISSN: 1939-1412
Shape-changing interfaces are a category of device capable of altering their form in order to facilitate communication of information. In this work, we present a shape-changing device that has been designed for navigation assistance. `The Animotus' (previously, `The Haptic Sandwich' ), resembles a cube with an articulated upper half that is able to rotate and extend (translate) relative to the bottom half, which is fixed in the user's grasp. This rotation and extension, generally felt via the user's fingers, is used to represent heading and proximity to navigational targets. The device is intended to provide an alternative to screen or audio based interfaces for visually impaired, hearing impaired, deafblind, and sighted pedestrians. The motivation and design of the haptic device is presented, followed by the results of a navigation experiment that aimed to determine the role of each device DOF, in terms of facilitating guidance. An additional device, `The Haptic Taco', which modulated its volume in response to target proximity (negating directional feedback), was also compared. Results indicate that while the heading (rotational) DOF benefited motion efficiency, the proximity (translational) DOF benefited velocity. Combination of the two DOF improved overall performance. The volumetric Taco performed comparably to the Animotus' extension DOF.
Spiers AJ, Liarokapis MV, Calli B, et al., 2016, Single-grasp object classification and feature extraction with simple robot hands and tactile sensors, IEEE Transactions on Haptics, Vol: 9, Pages: 207-220, ISSN: 1939-1412
Classical robotic approaches to tactile object identification often involve rigid mechanical grippers, dense sensor arrays, and exploratory procedures (EPs). Though EPs are a natural method for humans to acquire object information, evidence also exists for meaningful tactile property inference from brief, non-exploratory motions (a ‘haptic glance’). In this work, we implement tactile object identification and feature extraction techniques on data acquired during a single, unplanned grasp with a simple, underactuated robot hand equipped with inexpensive barometric pressure sensors. Our methodology utilizes two cooperating schemes based on an advanced machine learning technique (random forests) and parametric methods that estimate object properties. The available data is limited to actuator positions (one per two link finger) and force sensors values (eight per finger). The schemes are able to work both independently and collaboratively, depending on the task scenario. When collaborating, the results of each method contribute to the other, improving the overall result in a synergistic fashion. Unlike prior work, the proposed approach does not require object exploration, re-grasping, grasp-release, or force modulation and works for arbitrary object start positions and orientations. Due to these factors, the technique may be integrated into practical robotic grasping scenarios without adding time or manipulation overheads.
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