This is a survey article on the information transmission capability of haptic devices and ways of maximizing it. It is intended for readers who are engineering professionals interested in developing novel haptic interfaces for a variety of applications but are not necessarily trained in haptic science or human user research. We posit that the ultimate goal of any interface is the exchange of information between a machine and a user, and as such, the evaluation should involve estimating the information-transmission capability with human users. We conducted a literature survey on studies of haptic devices evaluated with human users using an information theoretic framework. Our goal was to discover and summarize best practices that can lead to high information transmission. The results confirmed findings from our own previous studies, uncovered new ways to effectively increase information transmission, and pointed to the need for broader dissemination of proper experimental methodology. We, therefore, present a concise yet comprehensive tutorial on psychophysical methodology for estimating information transfer (IT) and IT rate with humans, survey results on the typical IT achievable with haptic devices, and guidelines for maximizing information transmission with any human-machine interfaces. Although we focus on haptic systems, the information-theoretic framework, the psychophysical methods, and the guidelines presented in this article are applicable to other sensory modalities and multimodal interface systems also.

Recent studies have shown that decomposing spoken or written language into phonemes and transcribing each phoneme into a unique vibrotactile pattern enables people to receive lexical messages on the arm. A potential barrier to adopting this new communication system is the time and effort required to learn the association between phonemes and vibrotactile patterns. Therefore, in this study, we compared the learnability and generalizability of different learning approaches, including guided learning, self-guided learning, and a mnemonic device. We found that after 65 minutes of learning spread across 3 days, 67% of participants, including both native and non-native English speakers, following the guided learning could identify 100 haptic words with over 90% accuracy, while only 20% of participants using the self-guided learning paradigm could do so.

We present a study to examine one's learning and processing capacity of broadband tactile information, such as that derived from speech. In Study 1, we tested a user's capability to recognize tactile locations and movements on the forearm in the presence of masking stimuli and determined 9 distinguishable tactile symbols. We associated these symbols to 9 phonemes using two approaches, random and articulation associations. Study 2 showed that novice participants can learn both associations. However, performance for retention, construction of words and knowledge transfer to recognize unlearned words was better with articulation association. In study 3, we trained novel participants to directly recognize words before learning phonemes. Our results show that novel users can retain and generalize the knowledge to recognize new words faster when they were directly train on words. Finally, Study 4 examined optimal presentation rate for the tactile symbols without compromising learning and recognition rate.

In our daily lives, we rely heavily on our visual and auditory channels to receive information from others. In the case of impairment, or when large amounts of information are already transmitted visually or aurally, alternative methods of communication are needed. A haptic language offers the potential to provide information to a user when visual and auditory channels are unavailable. Previously created haptic languages include deconstructing acoustic signals into features and displaying them through a haptic device, and haptic adaptations of Braille or Morse code; however, these approaches are unintuitive, slow at presenting language, or require a large surface area. We propose using a multi-sensory haptic device called MISSIVE, which can be worn on the upper arm and is capable of producing brief cues, sufficient in quantity to encode the full English phoneme set. We evaluated our approach by teaching subjects a subset of 23 phonemes, and demonstrated an 86% accuracy in a 50 word identification task after 100 minutes of training.

We are developing a dual panel breast-dedicated positron emission tomography (PET) system using LSO scintillators coupled to position sensitive avalanche photodiodes (PSAPD). The charge output is amplified and read using NOVA RENA-3 ASICs. This paper shows that the coincidence timing resolution of the RENA-3 ASIC can be improved using certain list-mode calibrations. We treat the calibration problem as a convex optimization problem and use the RENA-3's analog-based timing system to correct the measured data for time dispersion effects from correlated noise, PSAPD signal delays and varying signal amplitudes. The direct solution to the optimization problem involves a matrix inversion that grows order (n(3)) with the number of parameters. An iterative method using single-coordinate descent to approximate the inversion grows order (n). The inversion does not need to run to convergence, since any gains at high iteration number will be low compared to noise amplification. The system calibration method is demonstrated with measured pulser data as well as with two LSO-PSAPD detectors in electronic coincidence. After applying the algorithm, the 511 keV photopeak paired coincidence time resolution from the LSO-PSAPD detectors under study improved by 57%, from the raw value of 16.3 ±0.07 ns full-width at half-maximum (FWHM) to 6.92 ±0.02 ns FWHM ( 11.52 ±0.05 ns to 4.89 ±0.02 ns for unpaired photons).

As analog and digital circuits have become more intertwined, we need to create a validation approach that handles both circuit types gracefully. This paper proposes a model-first approach, where one creates functional models of the analog blocks that will work in a HDL simulator, and then uses these models in the same way as HDL models are used for other standard cells: they are used in the full system validation, and the underlying implementations are validated to ensure they meet this specification. While creating functional models for the analog blocks might seem difficult, almost all analog blocks can be modeled as linear systems and we use this property to help create the required functional model.