Abstract
Humans commonly engage in upper limb haptic interactions during bipedal locomotion, and the expansive use of our arms makes us unique compared to the quadrupeds we evolved from. Examples of these haptic interactions include walking while carrying an object, using environmental surfaces such as a railing to provide stability assistance, and holding hands while walking with another individual. These interactions may increase the complexity of our locomotor behaviors, such as when feedforward control is employed to dampen arm motion and dissipate reaction forces at heel contact to stabilize an object we are carrying. However, these interactions also increase the available sensory information in the upper limb and can be utilized to aid in locomotor adaptation. For instance, the interaction forces experienced when holding hands or during collaborative object transport can lead to an unconscious synchronization of gait patterns between the two individuals. Recent work has further suggested that upper limb haptic interactions may have clinical relevance for improving locomotion in pathological populations. This review brings a novel, integrative perspective by examining upper limb haptic interactions in locomotion across everyday, collaborative, and clinical scenarios. In particular, the review highlights the importance of studying upper limb haptic interactions from different viewpoints to gain insight into the neuromechanical control of adaptive locomotion, as well as to investigate how these interactions can be exploited for clinical use.
Introduction
Human bipedal evolution from quadrupedal primates has facilitated the development of an extensive repertoire of upper limb movement tasks for our daily use (). Through evolution, the motor pathways associated with our upper limbs have strengthened (), and a simultaneous increase of the amount of space in the somatosensory cortex devoted to upper limb haptic sensation occurred (), which allow us to perform fine motor skills using our upper limbs with ease. It is important to note that standard human locomotion - without the addition of specific tasks for the upper limbs - involves a stereotypical antiphase arm swing trajectory, that is likely controlled by neural networks largely inherited from evolution including quadrupeds (; ; ). When performing upper limb tasks during locomotion, haptic information is available which can subsequently be useful in superimposing the execution of these skills within our basic locomotor pattern (). This includes signals from mechanoreceptors that provide information related to the interaction between our hand and the surface we are touching, muscle spindles indicating the positioning and movement of our upper limb, and Golgi tendon organs informing us of the muscle forces used during these interactions. This information can subsequently be used to inform feedforward control of upcoming movements, or for execution of movements in response to feedback, both of which are control mechanisms that are fundamental in adapting our locomotor patterns in response to upper limb haptic interactions.
This review will focus on upper limb tasks that are performed during locomotion and the haptic interactions associated with these behaviors. The tasks we will focus on include carrying of an object during locomotion (object transport), pairs of individuals carrying an object (collective object transport), hand holding (human-human interactive locomotion), and robots designed for guiding human locomotion (human-robot interactive locomotion), all of which are forms of adaptive locomotion (i.e., the modification of walking patterns in response to individual, environmental, or task constraints). This research will highlight how information from haptic interactions can be used to facilitate feedforward locomotor control, as well as provide non-verbal cues leading to adaptation of locomotor patterns. These haptic interactions have clinical implications and provide complementary methods for clinicians to reveal impairments (i.e., for diagnostic purposes) as well as to rehabilitate spatiotemporal gait characteristics and adaptive locomotion.
Use of haptics for control of object transport
Object transport in humans can take many forms, from the relatively simple task of carrying a backpack () to more complex head-supported load carrying tasks as seen in Nepalese transporters (). A unique characteristic of human locomotion is our ability to move around bipedally, allowing us to manipulate and control objects with our upper limbs as we walk. Humans frequently walk while simultaneously carrying an object (such as a cup of coffee), and more recently, walk while manipulating a cellular phone. Right after toddlers begin walking independently, they prefer to do so while carrying objects than with hands free, despite this added task would seemingly increase the complexity of motor coordination (). Chimpanzees have been observed carrying objects during locomotion, however, these actions are predominantly performed in a quadrupedal-tripedal manner ().
A fundamental requirement for carrying an object during locomotion is the generation of appropriate arm and finger forces to maintain the position and orientation of the object we are holding: think of walking with a cup of coffee (). Each contact our foot makes with the ground generates a significant force that is transferred upwards through our body segments and ultimately acts at the junction between our hand and the object (, ). This upward force occurs simultaneously with a downward inertial force of the object occurring from the sinusoidal vertical motion of our body with each stride we take (, ). Object transport then requires the appropriate generation of normal forces that are applied by the fingers to stabilize the object and prevent it from slipping or changing orientation. Using an instrumented object for transport, determined that the peak grip force lagged the downward peak inertial force of the object by less than 30 ms, which is shorter than the time required for a feedback-controlled motor response (Figure 1). These observations led to the conclusion that feedforward control mechanisms are utilized to generate the appropriate grip force to counteract the inertial forces of the object (). This is a form of anticipatory control, whereby the central nervous system regulates movement by anticipating future events as opposed to reacting after they occur. determined that anticipatory grip forces are appropriately scaled when transporting objects of different mass 0.25 m from a seated position. It is possible that a similar feedforward control mechanism is used when transporting objects of different mass when walking in order to prevent slippage of the object in response to inertial forces. Moreover, the trunk and object vertically oscillate in a coupled sinusoidal pattern when carrying an object, with the lowest point of the trunk trajectory coinciding with heel contact. When carrying loads of greater mass, the vertical oscillations of the trunk tend to be maintained (), however, this coincides with a reduction in trunk flexion/extension range of motion (), which may be facilitated with feedforward control. A damping ratio that relates the vertical range of motion of the trunk and object oscillations has been used to quantify the relationship between these trajectories. This ratio is affected by the modification of mechanical stiffness in the arm, acting to increase flexion at the minimum of the vertical trunk trajectory and decreasing flexion at the highest point (; ). For instance, when carrying an unstable object such as a container of water, the vertical movement of the object decreased relative to the movement of the trunk, suggesting an adjustment of arm stiffness to maintain the position of the container to avoid spillage (). The muscle activities responsible for these arm actions appear in a proximal to distal pattern and initiate prior to heel contact (), strengthening the argument supporting feedforward mechanisms controlling arm motion during object transport.
FIGURE 1
Object transportation can also be viewed in the context of internal models or internal representations of whole-body motion and environment. Locomotor body schema in primates may incorporate handheld objects, tools, footwear, and support surface positioning (
Finally, object transport requires coordination between the upper and lower limbs in order to provide appropriate upper limb stability to prepare for lower limb contact. For instance, grasping an object during walking requires a superposition of the voluntary movement upon the locomotor pattern, and a corresponding alignment of the two motor programs (
Transmission of haptic information through collective object transport
Not only do humans carry objects, but an important functional example of transport is when it is performed in a collective pair, adding a social component to the mechanical task. This results in an interactive locomotor pattern that must incorporate the collective relationship between neural circuity and biomechanical action within the pair. Collective object transport is generally performed in one of two methods: either in a “side-by-side” configuration or a “one person behind the other.”
Mechanically, side-by-side collective transport of a relatively light object (∼10% of collective participant mass) has little effect on the oscillation of the vertical center of mass trajectory when compared to walking alone (
In collective object transport, haptic information is transmitted between pairs of individuals through interaction forces in the object. For instance,
In terms of lower limb locomotor adaptations,
It should be noted that the rigidity or compliance (i.e., the deformation or change of shape) of the transported object is likely instrumental in the quality of the haptic interaction, as a greater amount of object deformation would alter the forces transmitted between participants. This issue has been highlighted in human-robot collective transport tasks, and methods have been developed to enhance the support from the robot while also avoiding excessive deformation of the transported object (
Human-human interaction as a form of haptic communication
Another physiologically relevant interactive locomotor task that humans commonly perform is walking hand in hand. This human-human interaction usually starts in infancy prior to the acquisition of independent locomotion (
One common result of haptic communication during hand in hand locomotion is that sensory information transferred between pairs may influence the walking patterns. Specifically, this task has been shown to result in an unconscious synchronization of lower limb movements between pairs (
Interestingly, evidence suggests that the level of walking synchronization between individuals holding hands uses attentional resources.
The magnitudes and directions of the interaction forces between hands are likely important for the haptic communication between paired individuals. These forces are generally less than 5 N (
FIGURE 2

Variability of spatial orientation of interaction forces during walking hand in hand. Representative interaction forces and arm/leg kinematics during non-synchronized and in-phase walking over five consecutive strides. From top to bottom: interaction forces (x, y, z, and 3d), contact side leg angle and lower limbs’ stance durations for each partner. At the bottom: spherical spatial density of the force vector directions during the corresponding five consecutive strides. The color scale indicates density diagrams calculated using the Kamb method for directional data. Note the increased variability of the force direction sphere during non-synchronized walking when compared to in-phase. Figure adapted from
The haptic communication occurring between pairs of individuals holding hands can also be used to provide non-verbal assistance for locomotor tasks. For instance, research has shown that haptic interaction forces of slightly larger magnitude than hand holding (10–30 N) are sufficient to provide guidance to coordinate locomotor patterns between leaders and followers (
There is currently a gap in the literature regarding the advantages and disadvantages of differing hand holding configurations, such as palm-to-palm or interlacing finger haptic interactions. Hand positioning has been suggested to be of importance during guidance of people with visual impairments. For instance, hand-over-hand manipulation tends to be more passive for the individual, while hand-under-hand is less controlling and facilitates increased haptic exploration during hand guidance tasks (
Developmental aspects
Haptic interaction with objects and people has a critical role also in the development of locomotion at an early age. Strikingly, toddlers who just started walking independently prefer to do so while carrying objects than with hands free (
Haptic exploration of the environment by toddlers during locomotion does not only involve the hands but can even be performed by the foot. Thus, it has been shown that, in contrast with adults, toddlers often place their feet onto an obstacle or across the edges of the stairs when stepping with the clear intent of obtaining knowledge of the object properties (
With regards to hand-to-hand interactions during walking, it has recently been shown that children (6–8 years old) exchange haptic communicative forces with the partner (whether an adult or another child) significantly different from those produced by dyads of adults only: children tend to be more compliant in the interaction than adults (
Technological advances: human-robot haptic interactions
Recent technological advances have resulted in the development of robots that provide a haptic link to participants through their hands, leading to adaptive locomotor responses. For example, robots such as Mako-no-te (
FIGURE 3

Locomotor adaptations during human-robot haptic interactions. (a) Participants walked forwards while holding the robot (“Slidey”) handles. A sensor embedded in the robot measured human-robot interaction forces. Motion capture data were used to calculate mean human velocity (v), step length (L), and step frequency (f) during steady-state walking. (b) The human-robot interaction controller consisted of transient velocity pulses at frequency fR and amplitude a superimposed on a trapezoidal velocity profile with bias b. We predicted that the robot’s mean velocity vR would affect human velocity while robot pulse frequency fR would affect human step frequency. (c) Mean gait parameters were calculated during four steps when mean human torso velocity v was constant. Note the synchronization between the robot oscillations and vertical foot position. Figure from
Clinical implications for haptic interactions
The use of upper limb haptic interactions has the potential to reveal impairments and provide additional information about corrupted sensorimotor neural processes, as well as complement the rehabilitation of locomotion and adaptive locomotion. For instance, upper limb compliance related to dynamic muscle tone is functionally significant in locomotor control (
Pathologies affecting the sensorimotor control of the upper limbs such as diabetic neuropathy, stroke, cerebellar ataxia, and cerebral palsy could potentially benefit from haptic training during rehabilitation. For example, past work has shown that an anchored railing placed next to a treadmill providing an opportunity for the hand to provide small levels of force can enhance stability in healthy (
It is worth stressing that there is a growing interest in investigating the rehabilitation of adaptive locomotion, since walking impairments may only present themselves during the performance of locomotor tasks and be absent from basic walking (
Given human-human interactive locomotion is related to the appearance of spontaneous synchronization of step patterns, patient-clinician handholding may provide an opportunity to unconsciously train targeted spatiotemporal gait characteristics. Also, since forced gait synchronization has been shown to be less beneficial to adapting asymmetric gait patterns between participants (
Statements
Author contributions
MM: Visualization, Conceptualization, Writing – review & editing, Writing – original draft. YI: Funding acquisition, Writing – review & editing, Project administration, Writing – original draft, Conceptualization, Visualization. PA: Writing – original draft, Writing – review & editing, Visualization. FL: Writing – review & editing. FS-L: Writing – review & editing, Visualization.
Funding
The author(s) declare financial support was received for the research and/or publication of this article. This work was supported by the Italian Ministry of Health and funded by the European Union- Next Generation EU- NRRPM6C2-Investment 2.1 Enhancement and strengthening of biomedical research in the NHS, PNRR-POC-2022-12375705.
Conflict of interest
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The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.
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Summary
Keywords
sensorimotor control, haptic communication, interaction forces, quadrupedal coordination, interactive locomotion
Citation
MacLellan MJ, Ivanenko Y, Avaltroni P, Lacquaniti F and Sylos-Labini F (2025) Sophisticated use of upper limb haptic interactions during adaptive locomotion. Front. Psychol. 16:1648450. doi: 10.3389/fpsyg.2025.1648450
Received
17 June 2025
Accepted
13 October 2025
Published
27 October 2025
Volume
16 - 2025
Edited by
Guy Cheron, Université Libre de Bruxelles, Belgium
Reviewed by
Sunghoon Shin, Yeungnam University, Republic of Korea
Ines Khiyara, University of Maine, United States
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Copyright
© 2025 MacLellan, Ivanenko, Avaltroni, Lacquaniti and Sylos-Labini.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Michael J. MacLellan, mimaclellan@upei.caYury Ivanenko, y.ivanenko@hsantalucia.it
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