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1. The Haptic Bridge: Towards a Theory for Haptic-Supported Learning

   https://doi.org/10.1145/3078072.3079755  

   Davis, Richard Lee; Orta Martinez, Melisa; Schneider, Oliver; MacLean, Karon E.; Okamura, Allison M.; Blikstein, Paulo (June 2017, Conference on Interaction Design and Children)

   Haptic force feedback systems are unique in their ability to dynamically render physical representations. Although haptic devices have shown promise for supporting learning, prior work mainly describes results of haptic-supported learning without identifying underlying learning mechanisms. To this end, we designed a haptic-supported learning environment and analyzed four students who used it to make connections between two different mathematical representations of sine and cosine: the unit circle, and their graph on the Cartesian plane. We highlight moments where students made connections between the representations, and identify how the haptic feedback supported these moments of insight. We use this evidence in support of a proposed theoretical and design framework for educational haptics. This framework captures four types of haptic representations, and focuses on one -- the haptic bridge -- that effectively scaffolds sense-making with multiple representations. 

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2. Haptic Training Simulation

   https://doi.org/10.3389/frvir.2020.00003  

   Lelevé, Arnaud; McDaniel, Troy; Rossa, Carlos (July 2020, Frontiers in Virtual Reality)

   null (Ed.)
3. Gait Sensing and Haptic Feedback Using an Inflatable Soft Haptic Sensor

   https://doi.org/10.1115/1.4064377  

   Quinones_Yumbla, Emiliano; Rokalaboina, Jahnav; Kanechika, Amber; Poddar, Souvik; Nibi, Tolemy M; Zhang, Wenlong (January 2024, ASME Letters in Dynamic Systems and Control)

   Abstract Collecting gait data and providing haptic feedback are essential for the safety and efficiency of robot-based rehabilitation. However, readily available devices that can perform both are scarce. This work presents a novel method for mutual sensing and haptic feedback, through the development of an inflatable soft haptic sensor (ISHASE). The design, modeling, and characterization of ISHASE are discussed. Four ISHASEs are embedded in the insole of a shoe to measure ground reaction forces and provide haptic feedback. Four participants were recruited to evaluate the performance of ISHASE as a sensor and haptic device. Experimental results indicate that ISHASE can accurately estimate user’s ground reaction forces while walking, with a maximum and a minimum accuracy of 91% and 85%, respectively. Haptic feedback was delivered to four different locations under the foot, and users could identify the location with an average 92% accuracy. A case study that exemplifies a rehabilitation scenario is presented to demonstrate ISHASE’s usefulness for mutual sensing and haptic feedback. 

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4. Haptic Communication of Language

   https://doi.org/10.1109/TOH.2023.3257539  

   Reed, Charlotte M.; Tan, Hong Z.; Jones, Lynette A. (April 2023, IEEE Transactions on Haptics)
5. Fluidically programmed wearable haptic textiles

   https://doi.org/10.1016/j.device.2023.100059  

   Jumet, Barclay; Zook, Zane A; Yousaf, Anas; Rajappan, Anoop; Xu, Doris; Yap, Te Faye; Fino, Nathaniel; Liu, Zhen; O’Malley, Marcia K; Preston, Daniel J (September 2023, Device)

   Haptic feedback offers a useful mode of communication in visually or auditorily noisy environments. The adoption of haptic devices in our everyday lives, however, remains limited, motivating research on haptic wearables constructed from materials that enable comfortable and lightweight form factors. Textiles, a material class fitting these needs and already ubiquitous in clothing, have begun to be used in haptics, but reliance on arrays of electromechanical controllers detracts from the benefits that textiles offer. Here, we mitigate the requirement for bulky hardware by developing a class of wearable haptic textiles capable of delivering high-resolution information on the basis of embedded fluidic programming. The designs of these haptic textiles enable tailorable amplitudinal, spatial, and temporal control. Combining these capabilities, we demonstrate wearables that deliver spatiotemporal cues in four directions with an average user accuracy of 87%. Subsequent demonstrations of washability, repairability, and utility for navigational tasks exemplify the capabilities of our approach. 

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