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1. Stiffness Perception using Transcutaneous Electrical Stimulation during Active and Passive Prosthetic Control

   https://doi.org/10.1109/EMBC44109.2020.9176078  

   Vargas, Luis; Huang, Helen; Zhu, Yong; Hu, Xiaogang (July 2020, International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC))

   null (Ed.)

   Haptic feedback allows an individual to identify various object properties. In this preliminary study, we determined the performance of stiffness recognition using transcutaneous nerve stimulation when a prosthetic hand was moved passively or was controlled actively by the subjects. Using a 2×8 electrode grid placed along the subject's upper arm, electrical stimulation was delivered to evoke somatotopic sensation along their index finger. Stimulation intensity, i.e. sensation strength, was modulated using the fingertip forces from a sensorized prosthetic hand. Object stiffness was encoded based on the rate of change of the evoked sensation as the prosthesis grasped one of three objects of different stiffness levels. During active control, sensation was modulated in real time as recorded forces were converted to stimulation amplitudes. During passive control, prerecorded force traces were randomly selected from a pool. Our results showed that the accuracy of object stiffness recognition was similar in both active and passive conditions. A slightly lower accuracy was observed during active control in one subject, which indicated that the sensorimotor integration processes could affect haptic perception for some users. 

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2. CollabAR: Edge-assisted Collaborative Image Recognition for Mobile Augmented Reality

   https://doi.org/10.1109/IPSN48710.2020.00-26  

   Liu, Zida; Lan, Guohao; Stojkovic, Jovan; Zhang, Yunfan; Joe-Wong, Carlee; Gorlatova, Maria (April 2020, ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN))

   Mobile Augmented Reality (AR), which overlays digital content on the real-world scenes surrounding a user, is bringing immersive interactive experiences where the real and virtual worlds are tightly coupled. To enable seamless and precise AR experiences, an image recognition system that can accurately recognize the object in the camera view with low system latency is required. However, due to the pervasiveness and severity of image distortions, an effective and robust image recognition solution for mobile AR is still elusive. In this paper, we present CollabAR, an edge-assisted system that provides distortion-tolerant image recognition for mobile AR with imperceptible system latency. CollabAR incorporates both distortion-tolerant and collaborative image recognition modules in its design. The former enables distortion-adaptive image recognition to improve the robustness against image distortions, while the latter exploits the `spatial-temporal' correlation among mobile AR users to improve recognition accuracy. We implement CollabAR on four different commodity devices, and evaluate its performance on two multi-view image datasets. Our evaluation demonstrates that CollabAR achieves over 96% recognition accuracy for images with severe distortions, while reducing the end-to-end system latency to as low as 17.8ms for commodity mobile devices. 

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3. Chop & Learn: Recognizing and Generating Object-State Compositions

   https://doi.org/10.1109/ICCV51070.2023.01852  

   Saini, Nirat; Wang, Hanyu; Swaminathan, Archana; Jayasundara, Vinoj; He, Bo; Gupta, Kamal; Shrivastava, Abhinav (October 2023, Proceedings of ICCV)

   Recognizing and generating object-state compositions has been a challenging task, especially when generalizing to unseen compositions. In this paper, we study the task of cutting objects in different styles and the resulting object state changes. We propose a new benchmark suite Chop & Learn, to accommodate the needs of learning objects and different cut styles using multiple viewpoints. We also propose a new task of Compositional Image Generation, which can transfer learned cut styles to different objects, by generating novel object-state images. Moreover, we also use the videos for Compositional Action Recognition, and show valuable uses of this dataset for multiple video tasks. Project website: https://chopnlearn.github.io. 

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4. SenseTribute: Smart Home Occupant Identification via Fusion Across On-Object Sensing Devices

   Han, Jun; Pan, Shijia; Sinha, Manal Kumar; Noh, Hae Young; Zhang, Pei; Tague, Patrick (November 2017, 4th ACM Int'l Conference on Systems for Energy-Efficient Built Environments (BuildSys'17))

   Occupant identification proves crucial in many smart home applications such as automated home control and activity recognition. Previous solutions are limited in terms of deployment costs, identification accuracy, or usability. We propose SenseTribute, a novel occupant identification solution that makes use of existing and prevalent on-object sensors that are originally designed to monitor the status of objects they are attached to. SenseTribute extracts richer information content from such on-object sensors and analyzes the data to accurately identify the person interacting with the objects. This approach is based on the physical phenomenon that different occupants interact with objects in different ways. Moreover, SenseTribute may not rely on users’ true identities, so the approach works even without labeled training data. However, resolution of information from a single on-object sensor may not be sufficient to differentiate occupants, which may lead to errors in identification. To overcome this problem, SenseTribute operates over a sequence of events within a user activity, leveraging recent work on activity segmentation. We evaluate SenseTribute using real-world experiments by deploying sensors on five distinct objects in a kitchen and inviting participants to interact with the objects. We demonstrate that SenseTribute can correctly identify occupants in 96% of trials without labeled training data, while per-sensor identification yields only 74% accuracy even with training data. 

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5. KeyStub: A Passive RFID-based Keypad Interface Using Resonant Stubs

   https://doi.org/10.1145/3631442  

   Nolan, John; Qian, Kun; Zhang, Xinyu (December 2023, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies)

   The proliferation of the Internet of Things is calling for new modalities that enable human interaction with smart objects. Recent research has explored RFID tags as passive sensors to detect finger touch. However, existing approaches either rely on custom-built RFID readers or are limited to pre-trained finger-swiping gestures. In this paper, we introduce KeyStub, which can discriminate multiple discrete keystrokes on an RFID tag. KeyStub interfaces with commodity RFID ICs with multiple microwave-band resonant stubs as keys. Each stub's geometry is designed to create a predefined impedance mismatch to the RFID IC upon a keystroke, which in turn translates into a known amplitude and phase shift, remotely detectable by an RFID reader. KeyStub combines two ICs' signals through a single common-mode antenna and performs differential detection to evade the need for calibration and ensure reliability in heavy multi-path environments. Our experiments using a commercial-off-the-shelf RFID reader and ICs show that up to 8 buttons can be detected and decoded with accuracy greater than 95%. KeyStub points towards a novel way of using resonant stubs to augment RF antenna structures, thus enabling new passive wireless interaction modalities. 

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