Out-of-session or “homework” engagement is a primary limiting factor in clinical mental health outcomes. Despite weekly practitioner contact, adherence to prescribed Digital Mental Health Interventions (DMHIs) typically drops by 96.1% within two weeks. We evaluate Ankor, a handheld shape-changing biofeedback physicalization, as an adjunct to standard audio-guided mindfulness. In a longitudinal randomized controlled study (N=69), participants were assigned to Ankor+audio or audio-only control across six weekly 15-minute laboratory sessions, with optional DMHI use between sessions. Relative to control, Ankor yielded a 351% increase in total DMHI practice initiations and, by week 2, maintained 29.4% active users versus 2.9% in control, indicating substantially higher out-of-session engagement and reduced early disengagement. These findings demonstrate the capacity of shape-changing biofeedback physicalizations to extend adherence to DMHIs, highlighting kinaesthetic interactions as a promising design pathway for sustaining engagement in mental health interventions.

Freehand pointing is a fundamental gesture commonly used for cursorless interactions. Prior work in HCI often elicits the same pointing behaviour—facing the target with an outstretched dominant arm and index finger. However, freehand pointing outside of HCI shows more variability across hand pose, usage, and coordination with gaze. To understand what variability exists and how it affects pointing performance, we collected data (N = 23) using a hybrid motion capture system. To elicit a wide variety of pointing behaviours, we included different levels of user effort and attention, as well as the widest range of target placements studied. We systematically characterised and described three distinct pointing behaviours, each with three different traits, ranging from accurate stereotypical pointing observed in prior works to more casual hip fire-style pointing. Our analysis demonstrates how different pointing behaviours affect pointing performance and highlights their importance when designing interactive systems for more naturalistic freehand pointing.

The force modality fundamentally transforms the interaction space of traditional touch input. When paired with compliant devices, which deform under force and provide immediate haptic feedback, there is potential to enhance user interactions significantly. However, the effects of compliance on force-input remain under-explored, with limited understanding of their full potential. This article presents the first systematic investigation of the impact of deformable, movable, and rigid surfaces on user performance and experience through three rigorous studies (each N = 28). The results reveal previously unreported effects, including (1) higher maximum comfortable forces on deformable surfaces, (2) user preference for soft and deformable surfaces over rigid surfaces, and (3) improved ability to maintain force input on softer surfaces. These results highlight the benefits of compliant surfaces, contrasting with the dominant use of force-input on rigid devices. These findings guide researchers and designers in optimizing user experience and performance of force-input interactions.

Despite significant advances in interactive clothing over the past decade, e-textiles lack traditional fabric robustness and comfort. SeamSleeve provides a method for using garment seams as sensing channels while retaining the benefits of regular clothing design. We power conductive seams at low voltages to stitch together everyday fabric panels, resulting in a novel sensing mechanism capable of detecting arm movements. Our first empirical study (n=10) identifies optimal seam design and placement on the sleeve, by comparing traditional seam forms in the context of sensing capabilities. A second study (n=14) demonstrates that our minimal sensing approach is capable of successfully classifying 8 arm exercises with an accuracy of 84%. Our findings support the effectiveness of the approach in areas such as longitudinal physiotherapeutic rehabilitation beyond the clinic, enabling everyday motion capture.

Objects are indispensable tools in our daily lives. Recent research has demonstrated their potential to act as conduits for digital interactions with microgestures, however, the primary focus was on situations where the hand firmly grasps an object. We introduce GraspUI, an exploratory design space of object-centric gestures within the seven distinct phases of the grasping process, spanning pre-, during, and post-grasp movements. We conducted ideation sessions with mixed-reality designers from industry and academia to explore gesture integration throughout the entire grasping process. The outcome was 38 storyboards envisioning practical applications. To evaluate the design space’s utility, we performed a video-based assessment with end-users. We then implemented an interactive prototype and quantified the overhead cost of performing proposed gestures through a secondary study. Participants reacted positively to gestures and could integrate them into existing usage of objects. To conclude, we highlight technical and usability guidelines for implementing and extending GraspUI systems.

We report findings from initial exploration into using electrotactile stimulation, on the surgeon's tongue, as a potential lower-latency and less mechanically-complex way to provide force-feedback to the operator of robot-assisted surgery. We conducted a pilot feasibility study wherein participants attempted to teleoperate a robot to grasp and lift chicken eggs without breaking or dropping them. The force measured by the robot's gripper was displayed differently based on the experimental condition: visually only, or visually with electrotactile tongue stimulation. Participants were more successful lifting eggs with tongue stimulation. Data from this preliminary study, along with insights from informal interviews, suggest that tongue stimulation has potential to enhance the efficacy and safety of robot-assisted surgery.

We introduce Pic2Tac, a novel system that automatically converts photographs into tactile images. It offers an alternative way to communicate visual information that is difficult to express using braille or alternative text. Current methods for creating tactile images are either limited in representation, or require handmade artefacts. Pic2Tac employs a unique approach that avoids a literal representation of image content (e.g. contours). Instead, it detects salient semantic content within photographs and translates them into tactile images using dedicated ‘tactile words’. Foreground objects are represented using icons, and patterns are used for background regions. The resulting binary image is printed on swell paper, where black regions rise to form a tactile image. Studies involving 60 participants, both sighted and with visual impairments, demonstrate the effectiveness of these tactile images in communicating semantic meaning. Our findings show that tactile and visual descriptions of scenes matched significantly. Overall, Pic2Tac is an affordable way to create accessible tactile images, costing only 1.50 USD per sheet.

Gestural interaction with freehands and while grasping an everyday object enables always-available input . To sense such gestures, minimal instrumentation of the user’s hand is desirable. However, the choice of an effective but minimal IMU layout remains challenging, due to the complexity of the multi-factorial space that comprises diverse finger gestures, objects, and grasps. We present SparseIMU , a rapid method for selecting minimal inertial sensor-based layouts for effective gesture recognition. Furthermore, we contribute a computational tool to guide designers with optimal sensor placement. Our approach builds on an extensive microgestures dataset that we collected with a dense network of 17 inertial measurement units (IMUs). We performed a series of analyses, including an evaluation of the entire combinatorial space for freehand and grasping microgestures (393 K layouts), and quantified the performance across different layout choices, revealing new gesture detection opportunities with IMUs. Finally, we demonstrate the versatility of our method with four scenarios.

Using microgestures, prior work has successfully enabled gestural interactions while holding objects. Yet, these existing methods are prone to false activations caused by natural finger movements while holding or manipulating the object. We address this issue with SoloFinger, a novel concept that allows design of microgestures that are robust against movements that naturally occur during primary activities. Using a data-driven approach, we establish that single-finger movements are rare in everyday hand-object actions and infer a single-finger input technique resilient to false activation. We demonstrate this concept’s robustness using a white-box classifier on a pre-existing dataset comprising 36 everyday hand-object actions. Our findings validate that simple SoloFinger gestures can relieve the need for complex finger configurations or delimiting gestures and that SoloFinger is applicable to diverse hand-object actions. Finally, we demonstrate SoloFinger’s high performance on commodity hardware using random forest classifiers.

Single-hand microgestures have been recognized for their potential to support direct and subtle interactions. While pioneering work has investigated sensing techniques and presented first sets of intuitive gestures, we still lack a systematic understanding of the complex relationship between microgestures and various types of grasps. This paper presents results from a user elicitation study of microgestures that are performed while the user is holding an object. We present an analysis of over 2,400 microgestures performed by 20 participants, using six different types of grasp and a total of 12 representative handheld objects of varied geometries and size. We expand the existing elicitation method by proposing statistical clustering on the elicited gestures. We contribute detailed results on how grasps and object geometries affect single-hand microgestures, preferred locations, and fingers used. We also present consolidated gesture sets for different grasps and object size. From our findings, we derive recommendations for the design of microgestures compatible with a large variety of handheld objects.

Over the last decades, there have been numerous efforts in wearable computing research to enable interactive textiles. Most work focus, however, on integrating sensors for planar touch gestures, and thus do not fully take advantage of the flexible, deformable and tangible material properties of textile. In this work, we introduce SmartSleeve, a deformable textile sensor, which can sense both surface and deformation gestures in real-time. It expands the gesture vocabulary with a range of expressive interaction techniques, and we explore new opportunities using advanced deformation gestures, such as, Twirl, Twist, Fold, Push and Stretch. We describe our sensor design, hardware implementation and its novel non-rigid connector architecture. We provide a detailed description of our hybrid gesture detection pipeline that uses learning-based algorithms and heuristics to enable real-time gesture detection and tracking. Its modular architecture allows us to derive new gestures through the combination with continuous properties like pressure, location, and direction. Finally, we report on the promising results from our evaluations which demonstrate real-time classification.