The relationship between the thermodynamic and computational properties of physical systems has been a major theoretical interest since at least the 19th century. It has also become of increasing practical importance over the last half-century as the energetic cost of digital devices has exploded. Importantly, real-world computers obey multiple physical constraints on how they work, which affects their thermodynamic properties. Moreover, many of these constraints apply to both naturally occurring computers, like brains or Eukaryotic cells, and digital systems. Most obviously, all such systems must finish their computation quickly, using as few degrees of freedom as possible. This means that they operate far from thermal equilibrium. Furthermore, many computers, both digital and biological, are modular, hierarchical systems with strong constraints on the connectivity among their subsystems. Yet another example is that to simplify their design, digital computers are required to be periodic processes governed by a global clock. None of these constraints were considered in 20th-century analyses of the thermodynamics of computation. The new field of stochastic thermodynamics provides formal tools for analyzing systems subject to all of these constraints. We argue here that these tools may help us understand at a far deeper level just how the fundamental thermodynamic properties of physical systems are related to the computation they perform.

The standard technology used to capture motion for biomechanical analysis in sports has employed marker-based optical systems. While these systems are excellent at providing positional information, they suffer from a limited ability to accurately provide fundamental quantities such as velocity and acceleration (hence forces and torques) during high-speed motion typical of many sports. Conventional optical systems require considerable setup time, can exhibit sensitivity to extraneous light, and generally sample too slowly to accurately capture extreme bursts of athletic activity. In recent years, wireless wearable sensors have begun to penetrate devices used in sports performance assessment, offering potential solutions to these limitations. This article, after determining pressing problems in sports that such sensors could solve and surveying the state-of-the-art in wearable motion capture for sports, presents a wearable dual-range inertial and magnetic sensor platform that we developed to enable an end-to-end investigation of high-level, very wide dynamic-range biomechanical parameters. We tested our system on collegiate and elite baseball pitchers, and have derived and measured metrics to glean insight into performance-relevant motion. As this was, we believe, the first ultra-wide-range wireless multipoint and multimodal inertial and magnetic sensor array to be used on elite baseball pitchers, we trace its development, present some of our results, and discuss limitations in accuracy from factors such as soft-tissue artifacts encountered with extreme motion. In addition, we discuss new metric opportunities brought by our systems that may be relevant for the assessment of micro-trauma in baseball.

We present Sonoflex, a thin-form, embroidered dynamic speaker made without using a permanent magnet. Our design consists of two flat spiral coils, stacked on top of each other, and is based on an isolated, thin (0.15 mm) enameled copper wire. Our approach allows for thin, lightweight, and textile speakers and does not require high voltage as in electrostatic speakers. We show how the speaker can be designed and fabricated and evaluate its acoustic properties as a function of manufacturing parameters (size, turn counts, turn spacing, and substrate materials). The experiment results revealed that we can produce audible sound with a broad frequency range (1.5 kHz - 20 kHz) with the embroidered speaker with a diameter of 50 mm. We conclude the paper by presenting several applications such as audible notifications and near-ultrasound communication.

Earphones have evolved from pure audio devices to "earables" that are capable of advanced sensing. Bespoke research devices have shown the unique sensing capabilities of the earable platform; however, they are hard to replicate and require expertise to develop in the first place. In this paper, we present OpenEarable 2.0 - an open source, unified platform that integrates a larger number of sensors for conducting comprehensive earable research. OpenEarable 2.0 works as regular binaural Bluetooth earphones and features two ultrasound capable microphones (inward/outward), a 3-axis ear canal accelerometer/bone microphone, a 9-axis head inertial measurement unit, pulse oximeter, optical temperature sensor, ear canal pressure sensor, and microSD card. These capabilities allow for the detection and measurement of 30+ phenomena on the ear that can be used across a wide range of applications in health monitoring, activity tracking, human-computer-interaction and authentication. We describe the design and development of OpenEarable 2.0 which follows best open hardware practices and achieves commercial-level wearability. We provide justification for the selection and placement of integrated sensors and include in-depth descriptions of the extensible, open source firmware and hardware that are implemented using free to use tools and frameworks. For real-time sensor control and data recording we also contribute a web-based dashboard and mobile smartphone app. The wearability and ability to sense different phenomena are validated in four studies which showcases how OpenEarable 2.0 provides accurate measurements in comparison to established gold-standard measurements. We further demonstrate that OpenEarable 2.0 can be assembled by inexperienced users, and that undergraduate students can build applications using the OpenEarable platform.

In this paper, we introduce the "Plug" sensor network, a ubiquitous networked sensing platform ideally suited to broad deployment in environments where people work and live. The backbone of the Plug sensor network is a set of 35 sensor-, radio-, and computation-enabled power strips distributed throughout the third floor of the MIT Media Lab. A single Plug device fulfills all the functional requirements of a normal power strip (i.e., four 120 V, 60 Hz electrical outlets; surge protector circuit; standard electrical connector to a US-style wall socket), and can be used without special training. Additionally, each Plug has a wide range of sensing modalities (e.g., sound, light, electrical current and voltage, vibration, motion, and temperature) for gathering data about how it is being used and its nearby environment. To our knowledge, the Plug sensor network is the first to embody the idea of designing sensor nodes to seamlessly become a part of their environment, rather than play the role of alien, if unobtrusive, observers. We argue this design principle is essential for sensor networks to succeed in the realm of ubiquitous computing. In this paper, we present an overview of the Plug hardware and software architectures, look at specific usage scenarios of a single Plug, and show example data taken across the entire Plug network to give a sense of the pulse of the building over a span of days. Finally, we present ongoing work interfacing heterogeneous devices with the Plug network for a variety of applications and discuss possible future work.