Wearable Soft Technologies for Haptic Sensing and Feedback

Virtual reality (VR) and augmented reality (AR) systems have garnered recent widespread attention due to increased accessibility, functionality, and affordability. These systems sense user inputs and typically provide haptic, audio, and visual feedback to blend interactive virtual environments with the real world for an enhanced or simulated reality experience. With applications ranging from immersive entertainment, to teleoperation, to physical therapy, further development of this technology has the potential for impact across multiple disciplines. However, VR/AR devices still face critical challenges that hinder integration into everyday life and additional applications; namely, the rigid and cumbersome form factor of current technology that is incompatible with the dynamic movements and pliable limbs of the human body. Recent advancements in the field of soft materials are uniquely suited to provide solutions to this challenge. Devices fabricated from flexible and elastic bio-compatible materials have significantly greater compatibility with the human body and could lead to a more natural VR/AR experience. This review reports state-of-the-art experimental studies in soft materials for wearable sensing and haptic feedback in VR/AR applications, explores emerging soft technologies for on-body devices, and identifies current challenges and future opportunities toward seamless integration of the virtual and physical world.

Adaptive wake and sleep detection for wearable systems

Walter Karlen

Sleep problems and disorders have a serious impact on human health and wellbeing. The rising costs for treating sleep-related chronic diseases in industrialized countries demands efficient prevention. Low-cost, wearable sleep / wake detection systems which give feedback on the wearer's "sleep performance" are a promising approach to reduce the risk of developing serious sleep disorders and fatigue. Not all bio-medical signals that are useful for sleep / wake discrimination can be easily recorded with wearable systems. Sensors often need to be placed in an obtrusive location on the body or cannot be efficiently embedded into a wearable frame. Furthermore, wearable systems have limited computational and energetic resources, which restrict the choice of sensors and algorithms for online processing and classification. Since wearable systems are used outside the laboratory, the recorded signals tend to be corrupted with additional noise that influences the precision of classification algorithms. In this thesis we present the research on a wearable sleep / wake classifier system that relies on cardiorespiratory (ECG and respiratory effort) and activity recordings and that works autonomously with minimal user interaction. This research included the selection of optimal signals and sensors, the development of a custom-tailored hardware demonstrator with embedded classification algorithms, and the realization of experiments in real-world environments for the customization and validation of the system. The processing and classification of the signals were based on Fourier transformations and artificial neural networks that are efficiently implementable into digital signal controllers. Literature analysis and empiric measurements revealed that cardiorespiratory signals are more promising for a wearable sleep / wake classification than clinically used signals such as brain potentials. The experiments conducted during this thesis showed that inter-subject differences within the recorded physiological signals make it difficult to design a sleep / wake classification model that can generalize to a group of subjects. This problem was addressed in two ways: First by adding features from another signal to the classifier, that is, measuring the behavioral quiescence during sleep using accelerometers. Conducted research on different feature extraction methods from accelerometer data showed that this data generalizes well for distinct subjects in the study group. In addition, research on user-adaptation methods was conducted. Behavioral sleep and wake measures, notably the measurement of reactivity and activity, were developed to build up a priori knowledge that was used to adapt the classification algorithm automatically to new situations. This thesis demonstrates the design and development of a low-cost, wearable hardware and embedded software for on-line sleep / wake discrimination. The proposed automatic user-adaptive classifier is advantageous compared to previously suggested classification methods that generalize over multiple subjects, because it can take changes in the wearer's physiology and sleep / wake behavior into account without adjustment from a human expert. The results of this thesis contribute to the development of smart, wearable, bio-physiological monitoring systems which require a high degree of autonomy and have only low computational resources available. We believe that the proposed sleep / wake classification system is a first promising step toward a context-aware system for sleep management, sleep disorder prevention, and reduction of fatigue.

EPFL2009

A Modular Approach towards Wearable Computing

Xavier Righetti

Thanks to recent improvements in electronics, conductive materials and energy storage capabilities, we are now able to embed small circuits but also ultra-mobile PCs (UMPC) into clothes. These devices are able to sense, process and communicate information between the user and his/her context. During the first research phase, we have developed a set of wearable applications that aimed at assisting the user in various situations such as the teleoperation of a blimp, or the pedestrian navigation in an unknown indoor environment. Research on both of these experiments can be classified in three categories: First the multimodal interaction between the users, his/her wearable interface, and the environment. Second, the context sensing such as the location of the user. Third, the wireless communication between the embedded system and the surrounding or remote devices. Although the resulting functionalities were satisfying as expected, both systems implied the use of cumbersome equipment such as a Head-Mounted Display (HMD), or a UMPC, and thus were too restrictive. With the experience gained from these experiments, we have decided to pursue a different approach. Indeed, in order to develop a truly wearable system, we have focused our research on the design and development of wearable modules based on small 8-bit microcontrollers. In our vision, one module equals one functionality. The advantages are threefold: First, the applications are virtually unlimited as the wearable modules can be attached or removed on the fly. Second, the user only wears what is needed, thus eliminating the superfluous hardware. Third, compared to commercial wearable products, the user can change the garment without changing the functionalities, or on the opposite, s/he can change the functionalities without changing the garment. A special attention has been put in the design of magnetic connectors in order to facilitate the placement of the modules on the garment. Compared to alternative approaches such as the lilypad from Leah Buechley, our modules do not need to be sewn to the garment and thus can very easily be relocated or removed when washing the garment for example. The decision to opt for a wired bus has been motivated by several factors. Indeed, not only the modules consume less power, but also they are smaller and lighter as they don't need wireless functionalities, nor individual batteries. We have developed specific applications in order to demonstrate and evaluate our modular approach. The feedback collected from test users and the results of various evaluation techniques showed us that our system is comfortable, attractive and perceived as flexible and creative. We believe that this kind of approach may not only foster the involvement of the user in wearable computing applications, but it can also be seen as a rapid prototyping platform consisting of a set of tools that facilitate the development of wearable applications.

EPFL2010

Wearable System for Spinal Cord Injury Rehabilitation with Muscle Fatigue Feedback

Sandro Carrara, Danilo Demarchi, Diego Marino, Sofia Milanese, Francesca Stradolini

The design, realization and verification of a system for personalized rehabilitation of Spinal Cord Injury (SCI) patients are here presented. It consists of two main parts: (i) one induces in the patient the physiotherapic movement proposed by the trainer with Functional Electrical Stimulation (FES) and (ii) the other integrates a muscle fatigue feedback evaluator based on acustic principle by wearable microphone. The importance of this fatigue feedback is crucial since SCI patients are unable to feel muscle soreness, which is an important parameter in FES rehabilitation. Indeed, the efficacy of this treatment is limited by the capacity of the muscle fibers to carry out the induced task. To optimize the feedback module, the performance of two different condenser microphones (analogic and digital) were compared each other as well as with the traditional method based on Electromyogram (EMG). Among these techniques, it was found that the digital microphone provides the best performance in fatigue estimation over time.