About

Gregory J. Gerling is a Professor in Systems Engineering based in Charlottesville, Virginia. His professional focus includes haptics, computational neuroscience, human factors, and biomechanics. He leads the Gerling Touch Lab, which involves research and mentorship of postdoctoral researchers and PhD students working on topics such as haptics, computational neuroscience, biomechanics, and related fields. Professor Gerling's work integrates multiple disciplines to advance understanding and applications in touch perception and human-machine interaction.

Research topics

• Computer Science
• Artificial Intelligence
• Computer vision
• Psychology
• Communication
• Cognitive psychology
• Mathematics
• Neuroscience
• Social psychology
• Geometry

Selected publications

• BPS2026 – Small adjustments in mechanotransduction currents drive receptive field sizes of single afferents and discrimination of monofilaments by populations

  Biophysical Journal · 2026-02-01

  article1st authorCorresponding
  PublisherDOI

• Coding of mechanical pain by myelinated and unmyelinated nociceptors in human hairy skin

  PAIN Reports · 2026-01-30

  articleOpen access

  Introduction: In humans, cutaneous Aβ afferents are traditionally linked to discriminative touch, while pain is attributed to Aδ and C fibers. However, we previously identified thickly myelinated high-threshold mechanoreceptors (Aβ-HTMRs) that encode noxious skin indentations and evoke painful percepts when selectively activated. These afferents also display finely grained receptive fields and resilience to fatigue during sustained stimulation. Objectives: To characterize the tuning properties of A-HTMRs under controlled mechanical stimulation and compare them with C-HTMRs. Methods: We used a robotic stimulation system capable of delivering precise skin indentations across a wide force range (20-1000 mN). Single-unit axonal recordings (microneurography) were obtained from cutaneous afferents of the radial nerve in awake healthy participants. Both low- and high-threshold mechanoreceptors were recorded. Results: Among 39 recorded mechanoreceptive afferents, HTMRs were distinguished by high mechanical thresholds and lack of response to soft brushing, with conduction velocities in the Aβ- and C-fiber ranges. Both A- and C-HTMRs exhibited force-dependent increases in spike count and firing rate, with A-HTMRs showing significantly stronger responses. Principal component analysis revealed distinct separation between A- and C-HTMRs, driven by A-HTMRs' robust high-force responses. Psychophysical testing indicated painful stimuli were often described as "sharp," and selective intraneural microstimulation of a single A-HTMR evoked localized "sharp-stinging" pain projected to its receptive field. Conclusion: Robot-controlled stimulation confirmed both A- and C-HTMRs' role in encoding painful mechanical stimuli. The fast conduction, high firing rates, fine receptive fields, and fatigue resilience of A-HTMRs suggest a specialized nociceptive system capable of conveying rich spatial-temporal information, potentially contributing to protective behaviors.

  PublisherDOI

• 3-D Reconstruction of Fingertip Deformation during Contact Initiation

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-02-10 · 1 citations

  preprintOpen access

  Abstract Dexterous manipulations rely on tactile feedback from the fingertips, which provides crucial information about contact events, object geometry, interaction forces, friction, and more. Accurately measuring skin deformations during tactile interactions can shed light on the mechanics behind such feedback. To address this, we developed a novel setup using 3-D digital image correlation (DIC) to both reconstruct the bulk deformation and local surface skin deformation of the fingertip under natural loading conditions. Here, we studied the local spatiotemporal evolution of the skin surface during contact initiation. We showed that, as soon as contact occurs, the skin surface deforms very rapidly and exhibits high compliance at low forces (<0.05 N). As loading and thus the contact area increases, a localized deformation front forms just ahead of the moving contact boundary. Consequently, substantial deformation extending beyond the contact interface was observed, with maximal amplitudes ranging from 5% to 10% at 5 N, close to the border of the contact. Furthermore, we found that friction influences the partial slip caused by these deformations during contact initiation, as previously suggested. Our setup provides a powerful tool to get new insights into the mechanics of touch and opens avenues for a deeper understanding of tactile afferent encoding.

  PublisherOA PDFDOI

• Detecting intervention-specific change in soft tissue mobility aligned with regional pain through optically measured skin surface strains

  medRxiv · 2025-08-01

  preprintOpen accessSenior authorCorresponding

  ABSTRACT Background Soft tissue manipulation is a widely used massage-based intervention for treating myofascial pain, yet its efficacy in increasing tissue mobility is primarily assessed through subjective clinical observations. While patient-reported outcomes often indicate symptom relief, the mechanical changes underlying these improvements remain unclear. No objective gold standard exists for assessing soft tissue mobility; current methods either measure stiffness in small tissue regions, which do not directly capture the lateral mobility between fascial layers, or rely on subjective assessments during clinical palpation. Optical measurement of tissue mobility from the skin surface, captured during hands-on assessment, offers an objective approach that is complementary with routine clinical practice. Methods This study used digital image correlation to capture skin surface deformation during hands-on assessment of the cervicothoracic region. Nineteen participants underwent a standardized soft tissue manipulation (STM) intervention protocol. Tissue mobility was measured immediately before and after intervention, considering tissue pull direction (superior vs. inferior relative to the participant’s back) and bilateral anatomy (left vs. right body sides). From these measurements, eleven strain-based biomarkers were derived to evaluate tissue glide and deformation. Results Across the population, several biomarkers changed significantly following intervention. At the individual level, STM intervention produced tissue mobility changes in nearly all treated participants, with 88% (15 of 17) improving in mobility on at least one body side. Among participants with baseline bilateral pain asymmetries, 90% showed greater mobility gains on their more painful side, reflecting alignment between tissue responsiveness and symptom severity. Baseline bilateral mobility asymmetries were observed in 53% of participants (10 of 19), eight of whom also reported pain asymmetries; in all cases, the less mobile side corresponded to the more painful side. Conclusions This study demonstrates that optically derived measures of tissue glide and deformation can objectively detect changes in soft tissue mobility immediately following an STM intervention. Mobility gains closely align with self-reported pain, supporting the clinical relevance of strain-based biomarkers. These findings underscore the potential of this quantitative approach to complement clinical assessments of myofascial restriction and therapeutic response.

  PublisherOA PDFDOI

• Strain-based biomarkers at the skin surface differentiate asymmetries in soft tissue mobility associated with myofascial pain

  Journal of the mechanical behavior of biomedical materials/Journal of mechanical behavior of biomedical materials · 2025-08-31

  articleOpen accessSenior authorCorresponding
  PublisherOA PDFDOI

• Mechanoreceptive Aβ primary afferents discriminate naturalistic social touch inputs at a functionally relevant time scale.

  PubMed · 2025-03-12

  preprintOpen accessSenior author

  Interpersonal touch is an important channel of social emotional interaction. How these physical skin-to-skin touch expressions are processed in the peripheral nervous system is not well understood. From microneurography recordings in humans, we evaluated the capacity of six subtypes of cutaneous mechanoreceptive afferents to differentiate human-delivered social touch expressions. Leveraging statistical and classification analyses, we found that single units of multiple mechanoreceptive Aβ subtypes, especially slowly adapting type II (SA-II) and fast adapting hair follicle afferents (HFA), can reliably differentiate social touch expressions at accuracies similar to human recognition. We then identified the most informative firing patterns of SA-II and HFA afferents, which indicate that average durations of 3-4 s of firing provide sufficient discriminative information. Those two subtypes also exhibit robust tolerance to spike-timing shifts of up to 10-20 ms, varying with touch expressions due to their specific firing properties. Greater shifts in spike-timing, however, can change a firing pattern's envelope to resemble that of another expression and drastically compromise an afferent's discrimination capacity. Altogether, the findings indicate that SA-II and HFA afferents differentiate the skin contact of social touch at time scales relevant for such interactions, which are 1-2 orders of magnitude longer than those for non-social touch.

  PublisherOA PDF

• Capturing 3D Skin Deformation and Finger Kinematics in Active Touch with Compliant Materials

  2025-07-08

  articleSenior author

  In touch interactions with compliant materials, such as soft fruits and tissues, an individual's finger movements elicit unique and nuanced cutaneous and kinesthetic cues. The qualities of such cues are key to our perceptual judgments, but due to the spatiotemporal complexity of human touch, its contact interactions are difficult to capture and analyze. Prior efforts to quantify these cues have mostly been limited to passive touch, which constrains one's finger orientation, point of contact, forces, and displacements. Moreover, studies in active touch paradigms often only consider contact with rigid plates. This work describes a novel approach to measure cutaneous skin deformation and kinesthetic digit movements while exploring compliant materials in active touch. We use digital image correlation to track 3D skin surface deformation and quantify its compressive and tensile strain, cross-sectional curvature, and contact area. Additionally, optical sensors are used to track digit movements and quantify their joint angles, normal displacement, penetration depth, and applied force. We measure the effects of varying stimulus compliance (45 and 184 kPa elastomers) on active contact in human-subjects experiments and identify that participant-specific trajectories indeed generate differentiable changes across several skin deformation cues.

  PublisherDOI

• 3-D Reconstruction of Fingertip Deformation During Contact Initiation

  Multisensory Research · 2025-10-14 · 3 citations

  articleOpen access

  Dexterous manipulations rely on tactile feedback from the fingertips, which provides crucial information about contact events, object geometry, interaction forces, friction, and more. Accurately measuring skin deformations during tactile interactions can shed light on the mechanics behind such feedback. To address this, we developed a novel setup using 3-D digital image correlation (DIC) to both reconstruct the bulk deformation and local surface skin deformation of the fingertip under natural loading conditions. Here, we studied the local spatiotemporal evolution of the skin surface during contact initiation. We showed that, as soon as contact occurs, the skin surface deforms very rapidly and exhibits high compliance at low forces (<0.05 N). As loading and thus the contact area increases, a localized deformation front forms just ahead of the moving contact boundary. Consequently, substantial deformation extending beyond the contact interface was observed, with maximal amplitudes ranging from 5% to 10% at 5 N, close to the border of the contact. Furthermore, we found that friction influences the partial slip caused by these deformations during contact initiation, as previously suggested. Our setup provides a powerful tool to get new insights into the mechanics of touch and opens avenues for a deeper understanding of tactile afferent encoding.

  PublisherOA PDFDOI

• Evaluating the Importance of Demographic and Technical Factors in Creating Authentic-Sounding AI-Generated Human Voice Clones

  2025-05-02 · 1 citations

  articleSenior author

  Business and governmental institutions face growing threats from synthetic audio deepfakes due to advances in voice cloning and artificial intelligence. By accessing a short recording of a person’s voice, malicious actors can clone it to say anything they like. This poses serious risks of fraud, identity theft, and loss of trust. While much prior research has explored defensive postures, limited works have considered the factors that make a cloned voice sound authentic. This effort investigates factors leading to more authentic sounding AI-generated clones of the human voice. A voice library of about 350 short samples was created, spanning a range of demographic (age, gender, ethnicity) and technical factors (cloning tool, training time, background noise). Using optimization techniques, a subset of 81 voices (67 cloned and 14 authentic) were selected for an online survey with human listeners ($\mathrm{n}=449$). Each voice was also assessed by the NISQA speech quality and naturalness model. Overall, human listeners perceived authentic voices as more realistic than cloned voices. However, subsets of cloned voices of certain technical and demographic factors were indistinguishable from authentic voices. Finally, human and machine generated ratings did not correlate, indicating that NISQA may evaluate voice authenticity in ways distinct from human listeners.

  PublisherDOI

• Developing Design Features to Facilitate AI-Assisted User Interactions

  2024-05-03 · 2 citations

  articleSenior author

  Interactive software tools employing generative artificial intelligence (AI) that help users formulate custom system queries are increasingly needed with growth in data quantities, relationships, and complexity. The need to afford such interactions is not new. Indeed, chatbots have long sought to bridge gaps between an individual’s intent and the system’s response. However, generative AI chatbots – in contrast to traditional chatbots that navigate pre-defined, rules-based decision trees – are unique in their promise to accept and respond to highly customized queries. At present though, most still rely upon the precise articulation of a structured prompt. The work herein develops and evaluates design features to facilitate AI-assistive user interactions in query formulation. The design features attempt to balance functional needs of users to make specific, goal-oriented, customized queries, with minimal constraints on exactly articulating pre-defined prompts. In a case study, we wireframe user interface prototypes in the domain of data log management, for evaluation with expert and novice users. Key elements of the design features revolve around the 1) refinement of search categories, 2) context-aware prompt recommendations, and 3) customization of query input per a user’s technical ability.

  PublisherDOI

Recent grants

• CRCNS: Neural Representations of Object Compliance in the Periphery

  NIH · $990k · 2017–2022

• Peripheral Mechanisms Governing Tactile Encoding During Normal Target Remodeling

  NIH · $2.5M · 2010–2018

• CHS: Small: Understanding the Percept of Soft Objects for Designing Naturalistic Touch Displays

  NSF · $508k · 2019–2023

Frequent coauthors

• Ellen A. Lumpkin

  University of California, Berkeley

  20 shared

• Steven C. Hauser

  University of Virginia

  16 shared

• Bingxu Li

  15 shared

• Håkan Olausson

  Linköping University

  15 shared

• Ian C. Sando

  St. Vincent Carmel Hospital

  13 shared

• Chang Xu

  University of Virginia

  12 shared

• Geb Thomas

  University of Iowa

  11 shared

• Saad S. Nagi

  Linköping University

  10 shared

Labs

• Gerling Touch LabPI

  Haptics, computational neuroscience, human factors, biomechanics

Awards & honors

• Senior Member of the IEEE
• Co-chair of the IEEE Haptics Symposium for 2018 and 2020
• Co-editor of the IEEE World Haptics Conference in 2017 and 2…
• Chair of the IEEE Technical Committee on Haptics
• Associate Editor of the IEEE Transactions on Haptics