Frequent blood pressure (BP) assessment is key to the diagnosis and treatment of many severe diseases, such as heart failure, kidney failure, hypertension, and hemodialysis. Current "gold-standard'' BP measurement techniques require the complete blockage of blood flow, which causes discomfort and disruption to normal activity when the assessment is done repetitively and frequently. Unfortunately, patients with hypertension or hemodialysis often have to get their BP measured every 15 minutes for a duration of 4-5 hours or more. The discomfort of wearing a cumbersome and limited mobility device affects their normal activities. In this work, we propose a device called eBP to measure BP from inside the user's ear aiming to minimize the measurement's impact on users' normal activities while maximizing its comfort level. eBP has 3 key components: (1) a light-based pulse sensor attached on an inflatable pipe that goes inside the ear, (2) a digital air pump with a fine controller, and (3) a BP estimation algorithm. In contrast to existing devices, eBP introduces a novel technique that eliminates the need to block the blood flow inside the ear, which alleviates the user's discomfort. We prototyped eBP custom hardware and software and evaluated the system through a comparative study on 35 subjects. The study shows that eBP obtains the average error of 1.8 mmHg and -3.1 mmHg and a standard deviation error of 7.2 mmHg and 7.9 mmHg for systolic (high-pressure value) and diastolic (low-pressure value), respectively. These errors are around the acceptable margins regulated by the FDA's AAMI protocol, which allows mean errors of up to 5 mmHg and a standard deviation of up to 8 mmHg.

We use a conductive fabric substrate as a building material for a soft sensor to extend the functionality of soft actuators. We use PCB etching techniques to apply a pattern to the fabric, yielding distinct conductive surfaces within the same textile. We connect these via flexible wire bus embedded in silicone, terminating in a flexible PCB. We show how touch and metal objects can be localized along the length of the composite fabric strip. We demonstrate an example soft robotic application, by replacing the constraint layer component in a PneuFlex-style soft actuator with the self contained sensing strip. We show that the augmented composite actuator is able to interact with conductive objects in the environment using a capacitive touch sensing with applications in grasping and human-robot interaction.

While several sensing modalities and transduction approaches have been developed for tactile sensing in robotic skins, there has been much less work towards extracting features for or identifying high-level gestures performed on the skin. In this paper, we investigate using deep neural networks with hidden Markov models (DNN-HMMs), geometric moments and gesture level features to identify a set of gestures performed on robotic skins. We demonstrate that these features are useful for identifying gestures, and predict a set of gestures from a 14-class dataset with 56% accuracy, and a 7-class dataset with 71% accuracy.

We present a fiber reinforced soft pneumatic actuator with integrated strain and pressure sensors. We demonstrate that combining these sensors into the same actuator enables proprioception of both actuator curvature and environmental contact forces. We describe the manufacture and integration of a simple liquid metal strain sensor, a pressure sensor, and electrical circuits used for the sensors. We derive a constant curvature model for the actuator which predicts actuator curvature from the air pressure, and other constants of manufacture. The utility of the sensor integration is demonstrated by using the actuator to distinguish successful grasps amongst a set of common cylindrical objects with varying diameter. The grasp radius is estimated from the relationship between the sensor pair. Contact forces with the environment (touch) may be inferred from sensor readings which deviate from unconstrained motion.

Assistive technology (AT) has the ability to improve the standard of living of those with disabilities, however, it can often be abandoned for aesthetic or stigmatizing reasons. Garment-based AT offers novel opportunities to address these issues as it can stay with the user to continuously monitor and convey relevant information, is non-invasive, and can provide aesthetically pleasing alternatives. In an effort to overcome traditional AT and wearable computing challenges including, cumbersome hardware constraints and social acceptability, we present Flutter, a fashion-oriented wearable AT. Flutter seamlessly embeds low-profile networked sensing, computation, and actuation to facilitate sensory augmentation for those with hearing loss. The miniaturized distributed hardware enables both textile integration and new methods to pair fashion with function, as embellishments are functionally leveraged to complement technology integration. Finally, we discuss future applications and broader implications of using such computationally-enabled textile wearables to support sensory augmentation beyond the realm of AT.

We present algorithms for gesture recognition using in-network processing in distributed sensor arrays embedded within systems such as tactile input devices, sensing skins for robotic applications, and smart walls. We describe three distributed gesture-recognition algorithms that are designed to function on sensor arrays with minimal computational power, limited memory, limited bandwidth, and possibly unreliable communication. These constraints cause storage of gesture templates within the system and distributed consensus algorithms for recognizing gestures to be difficult. Building up on a chain vector encoding algorithm commonly used for gesture recognition on a central computer, we approach this problem by dividing the gesture dataset between nodes such that each node has access to the complete dataset via its neighbors. Nodes share gesture information among each other, then each node tries to identify the gesture. In order to distribute the computational load among all nodes, we also investigate an alternative algorithm, in which each node that detects a motion will apply a recognition algorithm to part of the input gesture, then share its data with all other motion nodes. Next, we show that a hybrid algorithm that distributes both computation and template storage can address trade-offs between memory and computational efficiency.

We present an algorithm, analysis, and implementation of a six-channel range and bearing system for swarm robot systems with sizes in the order of centimeters. The proposed approach relies on a custom sensor and receiver model, and collection of intensity signals from all possible sensor/emitter pairs. This allows us to improve range calculation by accounting for orientation-dependent variations in the transmitted intensity, as well as to determine the orientation of the emitting robot. We show how the algorithm and analysis generalize to other range and bearing systems, and evaluate its performance experimentally using two ping-pong ball-sized “Droplets” mounted on a precise gantry system.

We present an omnidirectional powertrain for swarm robotic platforms that relies on low-cost vibration motors.We describe a mechanism and controller to achieve full 3-DoF motion on the plane. The proposed approach does not require the motors to be in phase, and overcomes differences in manufacturing by a hardware-in-the-loop auto-calibration routine based on the Nelder-Mead algorithm, which issues motion commands via infrared and records the resulting trajectories using an off-the-shelf webcam. We show convergence results of the calibration routine and sample trajectories of the swarm robotic platform “Droplet” demonstrating turning and omnidirectional drive.

We describe user interactions with the self-organized amorphous wall, a modular, fully distributed system of computational building blocks that communicate locally for creating smart surfaces and functional room dividers. We describe a menu and a widget-based approach in which functions are color-coded and can be selected by dragging them from module to module on the surface of the wall. We also propose an on-off switch gesture and a dial gesture each spanning multiple units as canonical input mechanisms that are realized in a fully distributed way.

We present the "Honeycomb", a microcontroller platform that can easily be networked into hexagonal lattices of hundreds of nodes to create novel materials that tightly integrate sensing, actuation, computation and communication. The tool-chain consists of the platforms, a viral boot-loader to virally disseminate programs into the network, a software library that facilitates sensing, control, and communication, and software tools that allow interacting with the network from a host computer. After a brief tutorial, participants will have an opportunity to experiment with the Honeycomb hardware, which will be made available during the studio, write code for distributed processing of sensor information, and drive various actuators ranging from multi-color lights to servo motors, with the goal to construct an interactive installation to be displayed at the conference. All materials, including open source hardware and software will be made available on the web prior to the studio.