The 20th century saw the rise of broadcast technologies that allowed us to instantly communicate with large numbers of people. In contrast, the 21st century has been about narrowcasting. The ability to send targeted information on a massive scale to specific individuals via their personal devices has changed how we live and created some of the most successful products and services in human history. However, our addiction to personal screens has had significant downsides. They isolate us from each other and our surroundings. Instead, we imagine a future where the world itself provides the personalized information, services and entertainment that we seek. This new capability will free us from the tyranny of our devices, impacting our lives in surprising and deeply profound ways.
In this paper, we present an approach for ball speed and spin estimation in table tennis based on a single racket-mounted inertial sensor. We conducted a research study comprising eight subjects performing different stroke types resulting in various ball speeds and spins. All ball-racket impacts were recorded with a high-speed camera for further evaluation. Firstly, several assumptions and simplifications of the unknown initial ball properties and the movements of player and racket had to be made. Secondly, the racket blade velocity right after impact was calculated. This was combined with a rebound model between the ball and the rubber to predict the speed and spin of the ball. Overall, the ball speed of strokes with forward spin could be estimated with an accuracy of 79.4% and for strokes with backward spin with an accuracy of 87.4%. The spin was estimated with an accuracy of 73.5% and 75.0%, respectively. To our knowledge, this contribution is the first attempt to estimate characteristics of rebounded balls with a single racket-mounted inertial sensor considering unknown initial conditions and constraints.
As advanced functional apparel (e.g. wearable technology) continues to develop and permeate the consumer market, sizing and fit for the human body have become obstacles to consumer accessibility and garment functionality. This study develops sizing and design strategies for an advanced functional compression garment for the lower leg through an investigation of anthropometric geometric variability of the North American civilian population (using the CAESAR database). We extracted six lower leg measurements - ankle, calf, and knee circumferences as well as knee-to ankle, knee-to-calf, and ankle-to-calf lengths - from a sample of CAESAR three-dimensional body scans (n = 160) and ran descriptive statistics to quantify lower leg variability.
We then arranged the sample population separately using six different grouping variables - body mass index (BMI), height, weight, knee-to-ankle length, ankle circumference, calf circumference, and knee circumference - and conducted an analysis of variance (ANOVA) using each sorting algorithm to determine which variable(s) produced the most distinct groups (quartiles) for the anthropometric dimensions of interest (e.g., lower leg circumferences/lengths).
The results conclude that sorting by BMI does not produce statistically discrete sizes; however, sorting by ankle circumference does (p < 0.05). Furthermore, length was found to be independent from circumference and vary consistently between ankle-based size groups. We conclude with sizing and design strategies for future development of advanced functional garments to aid in the transition from research to industry.
Stitched methods of e-textile fabrication offer durability and flexibility benefits, as well as relatively un-constrained layout patterns. The ability to affix discrete component packages without a PCB substrate would improve the overall flexibility and comfort of an e-textile garment. However, that level of integration between electronic and apparel manufacturing processes is difficult to achieve. Here we present the development of a stitched method of fabricating e-textile circuits with surface-mount components. The technique leverages technologies and methods common to sewn product manufacturing. Using a simulated high-intensity wear test, we evaluate the durability of this method for circuit architecture variables of component size, trace width, and trace orientation, as well as for methodological variables of solder deposition technique and reflow process. We show durability of 3% failure after a 14-hour wear test for the best manufacturing conditions. Further, we implement the manufacturing technique in an LED matrix display application.
Detecting fluid emissions (e.g. urination or leaks) that extend beyond containment systems (e.g. diapers or adult pads) is a cause of concern for users and developers of wearable fluid containment products. Immediate, automated detection would allow users to address the situation quickly, preventing medical conditions such as adverse skin effects and avoiding embarrassment. For product development, fluid emission detection systems would enable more accurate and efficient lab and field evaluation of absorbent products. This paper describes the development of a textile-based fluid-detection sensing method that uses a multi-layer "keypad matrix" sensing paradigm using stitched conductive threads. Bench characterization tests determined the effects of sensor spacing, spacer fabric property, and contact pressures on wetness detection for a 5mL minimum benchmark fluid volume. The sensing method and bench-determined requirements were then applied in a close-fitting torso garment for babies that fastens at the crotch (onesie) that is able to detect diaper leakage events. Mannequin testing of the resulting garment confirmed the ability of using wetness sensing timing to infer location of induced 5 mL leaks.
Sensing in e-textiles has generated great interest in a wide range of applications, including wetness detection. A common application for wetness detection sensors is the detection of urine in absorbent products such as diapers. However, unlike water, salinized liquids like urine accelerate the process of chemical reaction of integrated conductors (especially when exposed to electrical current), limiting the lifespan of e-textile sensors. This paper explores an approach to extending the lifespan of conductive thread sensors by blending copper filament with silver threads, and a comparative bench test of the effect of adding copper on the functional lifespan of a wetness sensor when exposed to saline. We find that while the bi-metallic sensor is effective in lengthening the lifespan of the conductive traces, it is likely due to the increased metal content rather than selective reaction of one metal to preserve the other. We make suggestions for further optimizing sensors.
We present a new method for building wearable electronic and sensor systems in which all components are permanently integrated directly into garments in high volume except for the battery. We discuss the design and construction of the first such fully-integrated sensor system, wearable heart rate monitoring garments (a sports bra, a compression short, and a compression shirt) that are machine washable. We demonstrate that heart rate measurements can be detected by our system's fabric-based electrodes. We also show experimental results from wash testing of the garment. The process described herein can be applied to the construction of computational and sensor systems for healthcare, sports, virtual reality, and first responder and military personnel monitoring, among others.
We present a novel unsupervised approach, UnADevs, for discovering activity clusters corresponding to periodic and stationary activities in streaming sensor data. Such activities usually last for some time, which is exploited by our method; it includes mechanisms to regulate sensitivity to brief outliers and can discover multiple clusters overlapping in time to better deal with deviations from nominal behaviour. The method was evaluated on two activity datasets containing large number of activities (14 and 33 respectively) against online agglomerative clustering and DBSCAN. In a multi-criteria evaluation, our approach achieved significantly better performance on majority of the measures, with the advantages that: (i) it does not require to specify the number of clusters beforehand (it is open ended); (ii) it is online and can find clusters in real time; (iii) it has constant time complexity; (iv) and it is memory efficient as it does not keep the data samples in memory. Overall, it has managed to discover 616 of the total 717 activities. Because it discovers clusters of activities in real time, it is ideal to work alongside an active learning system.
Interpreting datasets containing inertial data (acceleration, rate-of-turn, magnetic flux) requires a description of the datasets itself. Often this description is unstructured, stored as a convention or simply not available anymore. In this note, we argue that each modality exhibits particular statistical properties, which allows to reconstruct it solely from the sensor's data. To investigate this, tri-axial inertial sensor data from five publicly available datasets were analysed. Three statistical properties: mode, kurtosis, and number of modes are shown to be sufficient for classification - assuming the sampling rate and sample format are known, and that both acceleration and magnetometer data is present. While those assumption hold, 98% of all 1003 data points were correctly classified.
One of the main benefits of a wrist-worn computer is its ability to collect a variety of physiological data in a minimally intrusive manner. Among these data, electrodermal activity (EDA) is readily collected and provides a window into a person's emotional and sympathetic responses. EDA data collected using a wearable wristband are easily influenced by motion artifacts (MAs) that may significantly distort the data and degrade the quality of analyses performed on the data if not identified and removed. Prior work has demonstrated that MAs can be successfully detected using supervised machine learning algorithms on a small data set collected in a lab setting. In this paper, we demonstrate that unsupervised learning algorithms perform competitively with supervised algorithms for detecting MAs on EDA data collected in both a lab-based setting and a real-world setting comprising about 23 hours of data. We also find, somewhat surprisingly, that incorporating accelerometer data as well as EDA improves detection accuracy only slightly for supervised algorithms and significantly degrades the accuracy of unsupervised algorithms.
We present Smart Eye Mask, which is a portable and comfortable sleep sensing system that classifies sleep stages (REM or NREM) by using infrared sensors on an eye mask. Smart Eye Mask has 19 photo-reflectors and measures the change in eye movement from the distance between the eyelid and the device. Since Smart Eye Mask is integrated into an eye mask, users do not feel uncomfortable during sleep. Moreover, users can utilize it anywhere because eye masks are easy to carry. We conducted an evaluation of Smart Eye Mask, and the recognition rate of classifying sleep stages as REM or NREM was 80.0 %.
We present FingOrbits, a wearable interaction technique using synchronized thumb movements. A thumb-mounted ring with an inertial measurement unit and a contact microphone are used to capture thumb movements when rubbing against the other fingers. Spectral information of the movements are extracted and fed into a classification backend that facilitates gesture discrimination. FingOrbits enables up to 12 different gestures through detecting three rates of movement against each of the four fingers. Through a user study with 10 participants (7 novices, 3 experts), we demonstrate that FingOrbits can distinguish up to 12 thumb gestures with an accuracy of 89% to 99% rendering the approach applicable for practical applications.
Haptic displays are commonly limited to transmitting a discrete set of tactile motives. In this paper, we explore the transmission of real-valued information through vibrotactile displays. We simulate spatial continuity with three perceptual models commonly used to create phantom sensations: the linear, logarithmic and power model. We show that these generic models lead to limited decoding precision, and propose a method for model personalization adjusting to idiosyncratic and spatial variations in perceptual sensitivity. We evaluate this approach using two haptic display layouts: circular, worn around the wrist and the upper arm, and straight, worn along the forearm. Results of a user study measuring continuous value decoding precision show that users were able to decode continuous values with relatively high accuracy (4.4% mean error), circular layouts performed particularly well, and personalisation through sensitivity adjustment increased decoding precision.
This paper investigates sensitivity based prioritisation in the construction of tactile patterns. Our evidence is obtained by three studies using a wearable haptic display with vibrotactile motors (tactors). Haptic displays intended to transmit symbols often suffer the tradeoff between throughput and accuracy. For a symbol encoded with more than one tactor simultaneous onsets (spatial encoding) yields the highest throughput at the expense of the accuracy. Sequential onset increases accuracy at the expense of throughput. In the desire to overcome these issues, we investigate aspects of prioritisation based on sensitivity applied to the encoding of haptics patterns. First, we investigate an encoding method using mixed intensities, where different body locations are simultaneously stimulated with different vibration intensities. We investigate whether prioritising the intensity based on sensitivity improves identification accuracy when compared to simple spatial encoding. Second, we investigate whether prioritising onset based on sensitivity affects the identification of overlapped spatiotemporal patterns. A user study shows that this method significantly increases the accuracy. Furthermore, in a third study, we identify three locations on the hand that lead to an accurate recall. Thereby, we design the layout of a haptic display equipped with eight tactors, capable of encoding 36 symbols with only one or two locations per symbol.
In this paper, we present the VibrationCap system and its evaluation. Two main contributions are made. First, we provide recommendations for wearable- and technology-design of head-worn vibration displays. Second, we study how accurately individual vibrations can be localized on the head, depending on their position. The VibrationCap is an unobtrusive, inconspicuous, wireless head-worn vibration display completely integrated into a regular beanie. We use 19 vibration motors to apply vibrations of varying duration to predefined locations of the head. Using this prototype, we conducted a study with 20 participants on the ability to locate vibrations applied to small areas (ca. 1 cm2) of the head. We show that depending on the region, the ability to locate vibration can vary significantly. With this data we created a vibro-tactile localization accuracy mapping of the human head.
Smartwatches are designed for short interactions in varying mobile contexts. However little data is available on how present mobile conditions affect interaction with these devices. In this work, we investigate the effects of mobility (walking), encumbrance (by carrying items like shopping bags) and wearing the watch on the (non-) dominant hand on interaction techniques present with current devices: tapping targets, swiping, and flicking the wrist. The results showed that for tapping and swiping, the outfitted hand had the largest effect on selection time (9.41%, resp. 4.84% slower interaction when the watch was worn on the dominant hand), while for wrist flicking, encumbrance had the largest effect (11.94% slower when carrying bags). The walking condition had the largest effect on the error rate for all techniques. Swiping as an interaction technique was barely affected by any condition, both in terms of selection time and error rate, making it a robust mobile interaction technique for smartwatches.
We propose a sensing technique for detecting finger movements on the nose, using EOG sensors embedded in the frame of a pair of eyeglasses. Eyeglasses wearers can use their fingers to exert different types of movement on the nose, such as flicking, pushing or rubbing. These subtle gestures can be used to control a wearable computer without calling attention to the user in public. We present two user studies where we test recognition accuracy for these movements.
Skin-dragging is an emerging type of haptic feedback that coveys both precise spatial and temporal tactile cues through the motion of a small pin dragged across the skin. While past research focused on building skin-dragging wearable devices with different form-factors, and testing their feasibility, it is still unclear what the user's perception of such haptic stimuli is, and how designers should generate dragging motion-patterns for informative feedback to be presented on a finger. In this work, we attempt to answer these questions. We therefore asked designers to create dragging motions using changes of speed, direction and length. We then tested the generated skin-dragging motions with a haptic smart-ring, classified them and extracted guidelines that can be used to convey rich and informative feedback on the fingers.
Reaching out onto a shelf or into a cabinet and taking something out is a fundamental part of many activities. In our daily life we carry out several picking and placing actions from/to shelves, cupboards, racks, containers, etc.. With enough instrumentation identifying the shelf from which something has been taken is not a fundamental problem. However, reliably detecting specific shelf from which something has been taken with an unobtrusive and cheap setup remains, in general, an open problem. As a solution we developed WiCoSens - a wrist worn color sensor (CS) array to detect color coded surfaces. We describe the hardware design, the identification method and a simple color coded shelf setup to evaluate the system. Initial results show 100% accuracy with user independent training.
Advanced machine learning technologies are seldom applied to wearable motion sensor data obtained from sport movements. In this work, we therefore investigated neural networks for motion performance evaluation utilizing a set of inertial sensor-based ski jump measurements. A multi-dimensional convolutional network model that related the motion data under aspects of time, placement and sensor type was implemented. Additionally, its applicability as a measure for automatic motion style judging was evaluated. Results indicate that one multi-dimensional convolutional layer is sufficient to recognize relevant performance error representations. Furthermore, comparisons against a Support Vector Machine and a Hidden Markov Model show that the new model out-performs feature-based methods under noisy and biased data environments. Architectures such as the proposed evaluation system can hence become essential for automatic performance analysis and style judging systems in future.
The growing popularity of winter sports, as well as the trend towards high speed carving skis, have increased the risk of accidents on today's ski slopes. While many skiers now wear ski helmets, their bulk might in turn lower skiers' ability to sense their surroundings, potentially leading to dangerous situations. In this paper, we describe our "Smart" Ski Helmet (S-SH) prototype. S-SH uses a set of laser range finders mounted on the back to detect skiers approaching from behind and warns the wearer about potential collisions using three LEDs mounted at the helmet's front edge, slightly above the wearer's eye level. In this work, we describe a controlled experiment with 20 ski and snowboarding enthusiasts and a follow-up on-slope deployment with 6 additional participants of varying levels of expertise. Our findings indicate that the S-SH can significantly increase skiers' peripheral perception on traverse trails.
The emergence of smartwatches poses new challenges to information security. Although there are mature touch-based authentication methods for smartphones, the effectiveness of using these methods on smartwatches is still unclear. We conducted a user study (n=16) to evaluate how authentication methods (PIN and Pattern), UIs (Square and Circular), and display sizes (38mm and 42mm) affect authentication accuracy, speed, and security. Circular UIs are tailored to smartwatches with fewer UI elements. Results show that 1) PIN is more accurate and secure than Pattern; 2) Pattern is much faster than PIN; 3) Square UIs are more secure but less accurate than Circular UIs; 4) display size does not affect accuracy or speed, but security; 5) Square PIN is the most secure method of all. The study also reveals a security concern that participants' favorite method is not the best in any of the measures. We finally discuss implications for future touch-based smartwatch authentication design.
Every 98 seconds an American is sexually assaulted. Our work explores the use of on-body sensors to detect, communicate and prevent sexual assault. We present a stick-on clothing sensor which responds to initial signs of sexual assault such as disrobing to deter sexual abuse. The smart clothing operates in two modes: an active mode for instances when the victim is unconscious, and a passive mode where the victim can self-actuate the safety mechanism. Both modes alert the victim's friends and family, actuate an auditory alarm, activate odor-emitting capsules to create an immediate repulsion effect, and call emergency services. Our design is based on input from sexual assault survivors and college students who evaluated the clothing for aesthetic appeal, functionality, cultural sensitivity and their sense of personal safety. We show the practicality of our unobtrusive design with two user studies to demonstrate that our techno-social approach can help improve user safety and prevent sexual assault.
We introduce inScent, a wearable olfactory display that can be worn in mobile everyday situations and allows the user to receive personal scented notifications, i.e. scentifications. Olfaction, i.e. the sense of smell, is used by humans as a sensorial information channel as an element for experiencing the environment. Olfactory sensations are closely linked to emotions and memories, but also notify about personal dangers such as fire or foulness. We want to utilize the properties of smell as a notification channel by amplifying received mobile notifications with artificially emitted scents. We built a wearable olfactory display that can be worn as a pendant around the neck and contains up to eight different scent aromas that can be inserted and quickly exchanged via small scent cartridges. Upon emission, scent aroma is vaporized and blown towards the user. A hardware - and software framework is presented that allows developers to add scents to their mobile applications. In a qualitative user study, participants wore the inScent wearable in public. We used subsequent semi-structured interviews and grounded theory to build a common understanding of the experience and derived lessons learned for the use of scentifications in mobile situations.
The Dermal Abyss (d-abyss) presents an approach to biointerfaces in which the body surface is rendered as an interactive display by patterning biosensors into the skin to produce color changes in response to biomarker variations in the interstitial fluid. It combines advances in biotechnology with traditional methods in tattoo artistry. d-abyss is designed to use the aesthetics, permanence, and visible nature of tattoos to encode information. In the present work, we replace traditional inks with colorimetric and fluorescent biosensors that can report on the concentration of sodium, glucose, and pH in the interstitial fluid of the skin. We report the preliminary evaluation of these biosensors in an ex vivo skin model, assessing their visibility from the dermis. We describe different applications of d-abyss in the medical, lifestyle, and security domains. This work is a proof of concept of a platform in which the skin reveals information inside the body, tattoos form wearable displays within the skin, and the body's metabolism works as an input for the d-abyss biosensors.
Traditional intra-body communication approaches mostly rely on either fixed cable joints embedded in clothing, or on wireless radio transmission that tends to reach beyond the body. Situated between these approaches is body-coupled communication, a promising yet less-explored method that transmits information across the user's skin. We propose a novel body-coupled communication approach that simplifies the physical layer of data transmission via capacitive coupling between wearable systems with conductive fabrics: This layer provides a stable reference potential for the feedback path in proximity to the attached wearables on the human body, to cancel the erratic dependency on the environmental ground, and to increase the communications' reliability. Evaluation of our prototype shows significant increases in signal quality, due to reduced attenuation and noise. Requirements on hardware and, subsequently, energy consumption, cost, and implementation effort are reduced as well.
One of the first questions a researcher or designer of wearable technology has to answer in the design process is where on the body the device should be worn. It has been almost 20 years since Gemperle et al. wrote "Design for Wearability" , and although much of her initial guidelines on humans factors surrounding wearability still stand, devices and use cases have changed over time. This paper is a collection of literature and updated guidelines and reasons for on-body location depending on the use of the wearable technology and the affordances provided by different locations on the body.
Deep learning (DL) methods receive increasing attention within the field of human activity recognition (HAR) due to their success in other machine learning domains. Nonetheless, a direct transfer of these methods is often not possible due to domain specific challenges (e.g. handling of multi-modal sensor data, lack of large labeled datasets). In this paper, we address three key aspects for the future development of robust DL methods for HAR: (1) Is it beneficial to apply data specific normalization? (2) How to optimally fuse multimodal sensor data? (3) How robust are these approaches with respect to available training data? We evaluate convolutional neuronal networks (CNNs) on a new large real-world multimodal dataset (RBK) as well as the PAMAP2 dataset. Our results indicate that sensor specific normalization techniques are required. We present a novel pressure specific normalization method which increases the F1-score by ∼ 4.5 percentage points (pp) on the RBK dataset. Further, we show that late- and hybrid fusion techniques are superior compared to early fusion techniques, increasing the F1-score by up to 3.5 pp (RBK dataset). Finally, our results reveal that in particular CNNs based on a shared filter approach have a smaller dependency on the amount of available training data compared to other fusion techniques.
In cases where a user wears multiple devices, power supply and management for these devices can be a significant problem. In this research, we propose utilizing a wireless power transfer technique between items of clothing, with the aim of distributing power from a single (or a few) source(s) to multiple devices. Considering the power transfer between a pair of trousers and a shirt as a first step, we investigated three models of resonators attached to fabric on the surface of the clothing. Power transmission efficiencies were measured as a function of the misalignment of the resonator coils. The results showed that the helical model achieved stable wireless power transmission, while the single and array models could achieve higher efficiencies if the transmitter and receiver coils were kept in opportune alignment.
We developed wearable artificial eyes for people with visual impairment for achieving joint attention during communication. The glasses use sensors to recognize other person's head and face, and automatically generate similar gaze behavior. It can also detect the opponents gaze direction and inform the users by means of vibration motors for suggesting users to turn their head in the same direction, which supports joint attention skills. We have proposed several applications for our devices in this paper.
Contact-electrification or Triboelectric energy harvesting is an emerging area in the research of renewable energy sources. This paper illustrates an initial exploration of translating the existing models of triboelectric generators into woven textile structures. Seven woven samples made with single and double-weave structures that use Teflon, polyester, plastic and conductive materials as weft yarns are presented. These examples outline an initial proof of concept for incorporating a triboelectric harvesting setup within the textile substrate during the construction process. A continued investigation would be needed to further validate their performance.
User-defined foot-gesture is a promising approach to interacting with mobile devices when both hands are occupied. In this research, we first present a survey to identify how often interacting with mobile devices is needed when both hands are busy and how many tasks people commonly would like to do on the devices in such situations. We then present a study to compare a traditional approach with foot-gesture interaction in a simulated hands-occupied scenario. Results show that foot-gesture saved over 70% of the time compared with the traditional approach and was perceived more useful and satisfying.
In this paper, we present results on foot inclination angle estimation obtained with photo reflectors placed on a leg for a walking assist system employing electrical muscle stimulation. The photo reflectors detect the muscle shape deformation in accordance with the ankle movement while walking. We use nonparametric regression models to estimate the foot inclination angle from the sensor data. Experimental results with nine participants showed that a Gaussian process regression model was the best: the root mean squared error was 4.7 degrees and the Pearson's correlation coefficient was 0.97.
We conduct a study on visual comfort while reading using an opaque head-worn display (HWD) displaced laterally at 0°, 10°, 20° and 30°. Participants completed 30 minute sessions while reading fictional stories and rated their comfort level every five minutes. The 30° condition showed significantly worse comfort ratings than the other three conditions.
We present a design space for interaction with a ring, worn on the user's finger. Whilst prior work has studied the ring as a means of attachment for hand motion sensors, we investigate the interaction possibilities provided by the natural affordance of the ring form factor itself. Interactions include, for example, changing the placement of the ring on the fingers, moving the ring along or around a finger and spinning of the ring on a surface.
Alzheimer's is the 6th most leading cause of death in the United States, more lethal than breast and prostate cancer combined . Currently, there are over 5.5 million Americans suffering from this disease. Inspired by this concept, we created Motif, a wearable device to support members of our society who need it most. Motif is an aesthetically pleasing, auditory cueing system for individuals at risk or suffering from Alzheimer's. By playing songs in response to particular people, places and situations, Motif is able to trigger memories and provide context. Patients at risk for Alzheimer's or individuals struggling with memory concerns can greatly benefit from this wearable musical intervention, to improve their wellbeing and quality of life.
Active knit compression stockings are compression garments with integrated smart materials (i.e. shape memory alloy wires) that apply dynamic and controllable pressures to the body to provide therapeutic compression treatment for those suffering from orthostatic hypotension (OH). Current static compression garments (e.g. elastic knit garments) exert unpredictable pressures on the body and are difficult to don/doff, especially for elderly populations most effected by OH. Alternatively, dynamic compression garments currently on the consumer market (e.g. pneumatic compression wraps) are bulky, inhibit wearer mobility, and are usually tethered to an inflation source. Active knit compression stockings offer an alternative technology for compression therapy that leverage the architecture of traditional, weft knit fabric structures to create large, amplified contractions across the textile surface. The result is a new compression garment alternative that is simultaneously dynamic, mobile, and untethered.
Lumière is a jacket inspired from an automobile's signaling blinkers by allowing bikers to increase their visibility eliminating the need for hand signaling. The jacket was made of 90% silver based particle-free conductive ink for comfort, lightweight, and wearability. The particle free ink allowed infiltration into fabric, resulting in metallization of individual fibers. The jacket pattern is also specially designed after several user-tests.
In this paper three separate approaches to recording use data are encompassed in a single shoe sole prototype. A shoe sole with electronic and nonelectronic sensors is crafted using 3D printing of flexible materials from programed g-Code. The sole is inserted into a laser cut, 3D printing reinforced shoe upper. The shoe and sole are constructed from a design process of "Prototyping as a vehicle for inquiry" . The idea of slow technology is applied during the inquiry and new methods of non-electronic sensing are developed. The first approach uses flexible, conductive 3D printed material to construct pressure sensors in the shoe sole. The second approach uses photographic analysis and material color change to determine use over time. The third method creates micro geometric structures via code that are intended to break in specific ways with specific use. Although many hours are spent developing these approaches, the shoe serves as a research archetype that attempts to asks questions about precision, design and sustainability.
Augmented reality and wearable computing development has skyrocketed in the consumer product domain in the past few decades, while the open source domain remained rather neglected. We believe that this is due to the existing development platforms being expensive, closed-source and due to the lack of a strong open source augmented reality community.
We present a hardware and software open source wearable augmented reality platform which enables users to make augmented reality glasses that cost less than $250 and realize new applications of augmented reality which can then be added to the platform for others to use and contribute to.
Through the artifact of bio fiber art named Ava that inspired by Yin and Yang, this paper explores potential uses of traditional arts into current technology applications in bio wearable field. With the novelty of exploring prospective qualities in Kombucha textile complemented with the use of self-designed origami folds and electronics.
Sustainable self-grown microbial cellulose, Kombucha textile is used as an alternative wearable textile due to its unique properties. However, Kombucha also faces some flexibility limitations, therefore mathematical folds that origin from primitive techniques of Origami, is applied to reduce its limitations and enhance the textile's resiliency. Kombucha self-adhesive feature is completed with the use of latest components for light wiring circuit like SMD LEDs and conductive thread as a wire substitution. The combination of all this elements, stimulating the possibilities of wearable textile, giving a new form of bio e-textile with sustainable, practical and technological elements.
We present "hitoeCap", which has Poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT-PSS) textile electrodes for measuring electromyography (EMG) of the masticatory muscles. Masticatory muscle activity is relevant to several daily activities such as diet, sleep bruxism, and human motor control. Our aim is to leverage continuous EMG data of the masticatory muscles for monitoring daily activities. For motion artifact reduction, which is a major problem for measuring EMG with textile electrodes, we propose a new algorithm based on kinesiology by utilizing a correlation coefficient between the root mean square of the right side EMG and that of the left side EMG. We have developed a hitoeCap prototype and conducted experiments to verify the usability of the PEDOT-PSS electrodes and the feasibility of our proposed algorithm.
Based on the context of higher life expectancy and lower pregnancy rate, ageing society is unavoidable. Technological response is necessary. Hormone Couture exhibition presents two wearable technologies that aesthetically respond to women's reproductive hormone and body temperature changes. The designs address how technology creates a biopolitical issues whether the wearable technology empowers women by giving her access to biological information, or creates gendered oppression by having a technology that monitors, controls, and influences sexual behavior on the body. Both of these arguments cannot be separated as they altogether exist at the same time as two sides of the same coin. This contradictory relationship between technological advancement and gender politics was materialized into the work aiming to criticize and speculate the future of wearable technology, and how it reinforces dynamics of gender tension in society.
Cosmic Bitcasting emerges from the idea of connecting the human body with the universe by creating a wearable interface that can provide sensory feedback on the invisible cosmic radiation that passes through our bodies. The project proposes the creation of an open-source, wearable detector, that can detect secondary muons generated by cosmic rays hitting the Earth's atmosphere that penetrate the human body by triggering a series of embedded actuators (light, sound and vibration).
Wearable computing for the winter context is so far little explored. As well as low temperatures, winter brings the contrasts of darkness, and bright snow-reflected sunlight. We present the Breaking of the Dawn winter jacket, which detects the ambient light level and utilizes LEDs to light the darkness. Thermochromic ink patterning on the jacket dynamically changes, to inform the wearer of high UV light levels. The garment is made from reindeer leather, reflecting the traditional reindeer herding industry of the Arctic areas.
This project explores aesthetic territory at the intersection of wearable technologies with global traditional cultures and contemporary fashion design, providing alternative ways for people across cultures to express and communicate in the networked, hybrid physical-digital domain. It is a form of smart jewelry, a smart necklace, or collar that lays flat on the chest, employs wireless and multimedia computing, including animation, music, networking, and 3D printing.
Data Vows is a series of biometric sensing garments for near future commitment ceremonies. It imagines a world where these rituals are no longer sworn by words, but unlocked by displaying and exchanging our most sacred physical information: our heartbeat. In order to be wed, partners must trade heartbeat data to cement their devotion. When one partner is in close enough proximity, a custom crafted corset and a handmade kerchief illuminate to the rhythm of their combined heartbeats. This initial series is an exploration into wearable technology and e-textiles as ritual objects and design probes to critique embedded power structures within societal systems. Speculative design methods formed the framework, with traditions, textiles, modes of communication, and personal data as the components.
Haptic input is a common input method for navigation aids for visual impaired people, leveraging an otherwise unused sensory channel depending on the body region, but building systems with large numbers of vibration motors is rather complex.
For this purpose we developed a system to easily and quickly build systems with largen numbers of vibration motors. With only low requirements on manual skills as well as tools, such wearables with huge numbers of vibration motors can be reproduced, e.g., at a local Fab Lab or Makerspace.
A series of speculative and whimsical wearable devices that highlight the disconnect between our mind and body in sedentary lifestyle. This project aims to enhance our perception of how contemporary human-machine interfaces encourage and produce very particular body postures and gestures, which in turn shape behavior that becomes an automatic response. Keybod is a computer keyboard worn on the upper body that forces the wearer to be mindful of typing as well as their posture. Click-kick are shoe covers that work as a computer's mouse with existing energetic movements controlling its cursor. Text-neck is a collar piece that disrupts its wearer, helping them to avoid staying in a bad posture longer than desired when interacting with a smartphone.
LE MONSTRE is a responsive performance garment, changing the sound and projection of the performance space through audience interaction. As the audience is invited to investigate the garment through touch and pull, capacitive and resistive strain sensors relay the interaction as wifi MIDI signals. The garment was designed as an investigation into the technology and arts collaborative design process.