Extreme heat puts tremendous stress on human health and limits people’s ability to work, travel, and socialize outdoors. To mitigate heat in public spaces, thermal conditions must be assessed in the context of human exposure and space use. Mean Radiant Temperature (MRT) is an integrated radiation metric that quantifies the total heat load on the human body and is a driving parameter in many thermal comfort indices. Current sensor systems to measure MRT are expensive and bulky (6-directional setup) or slow and inaccurate (globe thermometers) and do not sense space use. This engineering systems paper introduces the hardware and software setup of a novel, low-cost thermal and visual sensing device (MaRTiny). The system collects meteorological data, concurrently counts the number of people in the shade and sun, and streams the results to an Amazon Web Services (AWS) server. MaRTiny integrates various micro-controllers to collect weather data relevant to human thermal exposure: air temperature, humidity, wind speed, globe temperature, and UV radiation. To detect people in the shade and Sun, we implemented state of the art object detection and shade detection models on an NVIDIA Jetson Nano. The system was tested in the field, showing that meteorological observations compared reasonably well to MaRTy observations (high-end human-biometeorological station) when both sensor systems were fully sun-exposed. To overcome potential sensing errors due to different exposure levels, we estimated MRT from MaRTiny weather observations using machine learning (SVM), which improved RMSE. This paper focuses on the development of the MaRTiny system and lays the foundation for fundamental research in urban climate science to investigate how people use public spaces under extreme heat to inform active shade management and urban design in cities.
more »
« less
Mobile Device Batteries as Thermometers
The ability to sense ambient temperature pervasively, albeit crucial for many applications, is not yet available, causing problems such as degraded indoor thermal comfort and unexpected/premature shutoffs of mobile devices. To enable pervasive sensing of ambient temperature, we propose use of mobile device batteries as thermometers based on (i) the fact that people always carry their battery-powered smart phones, and (ii) our empirical finding that the temperature of mobile devices' batteries is highly correlated with that of their operating environment. Specifically, we design and implement Batteries-as-Thermometers (BaT), a temperature sensing service based on the information of mobile device batteries, expanding the ability to sense the device's ambient temperature without requiring additional sensors or taking up the limited on-device space. We have evaluated BaT on 6 Android smartphones using 19 laboratory experiments and 36 real-life field-tests, showing an average of 1.25°C error in sensing the ambient temperature.
more »
« less
- Award ID(s):
- 1739577
- NSF-PAR ID:
- 10301366
- Date Published:
- Journal Name:
- Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
- Volume:
- 4
- Issue:
- 1
- ISSN:
- 2474-9567
- Page Range / eLocation ID:
- 1 to 21
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Context-based pairing solutions increase the usability of IoT device pairing by eliminating any human involvement in the pairing process. This is possible by utilizing on-board sensors (with same sensing modalities) to capture a common physical context (e.g., ambient sound via each device’s microphone). However, in a smart home scenario, it is impractical to assume that all devices will share a common sensing modality. For example, a motion detector is only equipped with an infrared sensor while Amazon Echo only has microphones. In this paper, we develop a new context-based pairing mechanism called Perceptio that uses time as the common factor across differing sensor types. By focusing on the event timing, rather than the specific event sensor data, Perceptio creates event fingerprints that can be matched across a variety of IoT devices. We propose Perceptio based on the idea that devices co-located within a physically secure boundary (e.g., single family house) can observe more events in common over time, as opposed to devices outside. Devices make use of the observed contextual information to provide entropy for Perceptio’s pairing protocol. We design and implement Perceptio, and evaluate its effectiveness as an autonomous secure pairing solution. Our implementation demonstrates the ability to sufficiently distinguish between legitimate devices (placed within the boundary) and attacker devices (placed outside) by imposing a threshold on fingerprint similarity. Perceptio demonstrates an average fingerprint similarity of 94.9% between legitimate devices while even a hypothetical impossibly well-performing attacker yields only 68.9% between itself and a valid device.more » « less
-
more » « less
Abstract Massive deployments of wireless sensor nodes (WSNs) that continuously detect physical, biological or chemical parameters are needed to truly benefit from the unprecedented possibilities opened by the Internet-of-Things (IoT). Just recently, new sensors with higher sensitivities have been demonstrated by leveraging advanced on-chip designs and microfabrication processes. Yet, WSNs using such sensors require energy to transmit the sensed information. Consequently, they either contain batteries that need to be periodically replaced or energy harvesting circuits whose low efficiencies prevent a frequent and continuous sensing and impact the maximum range of communication. Here, we report a new chip-less and battery-less tag-based WSN that fundamentally breaks any previous paradigm. This WSN, formed by off-the-shelf lumped components on a printed substrate, can sense and transmit information without any need of supplied or harvested DC power, while enabling full-duplex transceiver designs for interrogating nodes rendering them immune to their own self-interference. Also, even though the reported WSN does not require any advanced and expensive manufacturing, its unique parametric dynamical behavior enables extraordinary sensitivities and dynamic ranges that can even surpass those achieved by on-chip sensors. The operation and performance of the first implementation of this new WSN are reported. This device operates in the Ultra-High-Frequency range and is capable to passively and continuously detect temperature changes remotely from an interrogating node.
-
Recent advancements in wearable technology have improved lifestyle and medical practices, enabling personalized care ranging from fitness tracking, to real-time health monitoring, to predictive sensing. Wearable devices serve as an interface between humans and technology; however, this integration is far from seamless. These devices face various limitations such as size, biocompatibility, and battery constraints wherein batteries are bulky, are expensive, and require regular replacement. On-body energy harvesting presents a promising alternative to battery power by utilizing the human body’s continuous generation of energy. This review paper begins with an investigation of contemporary energy harvesting methods, with a deep focus on piezoelectricity. We then highlight the materials, configurations, and structures of such methods for self-powered devices. Here, we propose a novel combination of thin-film composites, kirigami patterns, and auxetic structures to lay the groundwork for an integrated piezoelectric system to monitor and sense. This approach has the potential to maximize energy output by amplifying the piezoelectric effect and manipulating the strain distribution. As a departure from bulky, rigid device design, we explore compositions and microfabrication processes for conformable energy harvesters. We conclude by discussing the limitations of these harvesters and future directions that expand upon current applications for wearable technology. Further exploration of materials, configurations, and structures introduce interdisciplinary applications for such integrated systems. Considering these factors can revolutionize the production and consumption of energy as wearable technology becomes increasingly prevalent in everyday life.more » « less
-
more » « less
Abstract Recent years have witnessed the rapid development of sustainable materials. Along this line, developing biodegradable or recyclable soft electronics is challenging yet important due to their versatile applications in biomedical devices, soft robots, and wearables. Although some degradable bulk hydrogels are directly used as the soft electronics, the sensing performances are usually limited due to the absence of distributed conducting circuits. Here, sustainable hydrogel‐based soft electronics (HSE) are reported that integrate sensing elements and patterned liquid metal (LM) in the gelatin–alginate hybrid hydrogel. The biopolymer hydrogel is transparent, robust, resilient, and recyclable. The HSE is multifunctional; it can sense strain, temperature, heart rate (electrocardiogram), and pH. The strain sensing is sufficiently sensitive to detect a human pulse. In addition, the device serves as a model system for iontophoretic drug delivery by using patterned LM as the soft conductor and electrode. Noncontact detection of nearby objects is also achieved based on electrostatic‐field‐induced voltage. The LM and biopolymer hydrogel are healable, recyclable, and degradable, favoring sustainable applications and reconstruction of the device with new functions. Such HSE with multiple functions and favorable attributes should open opportunities in next‐generation electronic skins and hydrogel machines.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1145/3381015
