Multisensory Wearable Motion Analysis in Spine Biomechanics

Textile based piezoresistive transducers are an innovative category of devices that use yarns made of conductive and elastic fibers or screen printed conductive rubber coatings to sense strain. They usually satisfy wearability requirements and are used in real-time information gathering systems, being comfortable, ubiquitous and available for long term monitoring. They include knitted fiber transducers (KFTs), sewed fiber transducers (SFTs) and smeared redundant elastomer (SRETs) transducers. In the latter category, SRETs constituted by Conductive Elastomers (CEs) have been commonly employed as strain sensors networks to detect human posture and gesture. Elastic interconnection wiring is also easily realized leading to monolithic fabrication techniques which avoid the presence of metal wires and multiple solderings. Despite this, there is a strong dependence of the system performance by the body structure garment fitting. Moreover, the non-linear dynamical behaviour of SRETs requires identification algorithms, functions which relate joint angles to electrical values presented by the sensor network. The construction of these functions is quite complex and time of computation dramatically increases with the number of degrees of freedom and with the accuracy required to the system to be resolved. Recent development of CEs sensor modeling overcomed some of their main limitations and introduced new fields of operability in SRETs networks. In particular, in strain applications, a useful data processing technique is presented for treating the non-linear dynamical response, considering the different behaviour in sensor elongation and relaxation: actually, when the sensor in stretched, the breakdown of carbon black agglomerates produces an increase in resistance. Inversely, when the sensor is relaxed, the cross-link readjustment lead to different electrically conductive paths in respect to the previous states. This technique has found its implementation in multisensory systems, leading to encouraging results in biomechanical reconstruction. Furthermore, a novel approach in CEs sensing is described, relating the global curvature of a layer to its electrical resistance value variation and exploring under which conditions the resistance can be considered uncorrelated with its particular local bending profile. These devices are called Smeared Conductive Elastomer Electrogoniometers (SCEEGs) and under particular configuration they can be employed as on-body electrogoniometers. The integration of SRET arrays and SCEEGs is definitely a powerful tool for human body posture and gesture reconstruction through efficient and fast algorithms. Moreover In this study we introduce a particular realization of CE strain sensors deposed on an adhesive taping, obtaining a very low skin artifact device (VLSA). We present an electrogoniometric system in which the inextensible insulating layer has been replaced by an elastic layer, allowing the system to be employed both as strain sensor and as electrogoniometer. Finally, we present a biomechanical application in lumbar spine posture monitorization. As a matter of fact, it is known from literature that in the sagittal balance there is a strong correlation with the torso angle and geometrical parameters of lumbar vertebraes, such as the angle between subsequent upper endplates. Data obtained from piezoresistive sensors are so suitable to be used in biomechanical analysis in order to predict forces and moments acting on the functional spinal units

Wearable Textile Platform for Assessing Stroke Patient Treatment in Daily Life Conditions

Monitoring physical activities during post-stroke rehabilitation in daily life may help physicians to optimize and tailor the training program for patients. The European research project INTERACTION (FP7-ICT-2011-7-287351) evaluated motor capabilities in stroke patients during the recovery treatment period. We developed wearable sensing platform based on the sensor fusion among inertial, knitted piezoresistive sensors and textile EMG electrodes. The device was conceived in modular form and consists of a separate shirt, trousers, glove, and shoe. Thanks to the novel fusion approach it has been possible to develop a model for the shoulder taking into account the scapulo-thoracic joint of the scapular girdle, considerably improving the estimation of the hand position in reaching activities. In order to minimize the sensor set used to monitor gait, a single inertial sensor fused with a textile goniometer proved to reconstruct the orientation of all the body segments of the leg. Finally, the sensing glove, endowed with three textile goniometers and three force sensors showed good capabilities in the reconstruction of grasping activities and evaluating the interaction of the hand with the environment, according to the project specifications. This paper reports on the design and the technical evaluation of the performance of the sensing platform, tested on healthy subjects

Ultrafast humidity sensor based on liquid phase exfoliated graphene

Humidity sensing is important to a variety of technologies and industries, ranging from environmental and industrial monitoring to medical applications. Although humidity sensors abound, few available solutions are thin, transparent, compatible with large-area sensor production and flexible, and almost none are fast enough to perform human respiration monitoring through breath detection or real-time finger proximity monitoring via skin humidity sensing. This work describes chemiresistive graphene-based humidity sensors produced in few steps with facile liquid phase exfoliation (LPE) followed by Langmuir-Blodgett assembly that enables active areas of practically any size. The graphene sensors provide a unique mix of performance parameters, exhibiting resistance changes up to 10% with varying humidity, linear performance over relative humidity (RH) levels between 8% and 95%, weak response to other constituents of air, flexibility, transparency of nearly 80%, and response times of 30 ms. The fast response to humidity is shown to be useful for respiration monitoring and real-time finger proximity detection, with potential applications in flexible touchless interactive panels.Comment: 18 pages, 13 figure

Wearable Nano-Based Gas Sensors for Environmental Monitoring and Encountered Challenges in Optimization

With a rising emphasis on public safety and quality of life, there is an urgent need to ensure optimal air quality, both indoors and outdoors. Detecting toxic gaseous compounds plays a pivotal role in shaping our sustainable future. This review aims to elucidate the advancements in smart wearable (nano)sensors for monitoring harmful gaseous pollutants, such as ammonia (NH3), nitric oxide (NO), nitrous oxide (N2O), nitrogen dioxide (NO2), carbon monoxide (CO), carbon dioxide (CO2), hydrogen sulfide (H2S), sulfur dioxide (SO2), ozone (O3), hydrocarbons (CxHy), and hydrogen fluoride (HF). Differentiating this review from its predecessors, we shed light on the challenges faced in enhancing sensor performance and offer a deep dive into the evolution of sensing materials, wearable substrates, electrodes, and types of sensors. Noteworthy materials for robust detection systems encompass 2D nanostructures, carbon nanomaterials, conducting polymers, nanohybrids, and metal oxide semiconductors. A dedicated section dissects the significance of circuit integration, miniaturization, real-time sensing, repeatability, reusability, power efficiency, gas-sensitive material deposition, selectivity, sensitivity, stability, and response/recovery time, pinpointing gaps in the current knowledge and offering avenues for further research. To conclude, we provide insights and suggestions for the prospective trajectory of smart wearable nanosensors in addressing the extant challenges

A wearable sensor to monitor localized sweat rate as support tool for monitoring athletes' performances

Objectives We developed a wearable sensor for the real time measurement of sweat rate in localized areas of the human body. This sensor represents the first step in the development of a wearable sensor network capable to estimate the global sweat rate via an ad hoc algorithm. Such device would be used to monitor athletes' hydration status during training and improve their performances. Equipment and Methods For this study, we tested our sensor on thirteen football players during a cycling test on a cycle ergometer. The sweat rate sensor was compared to a medical device that, although measuring a different physiological process, provides discrete data based on the same working principle, i.e. the diffusion of the water vapour emitted from the skin. Results Our sensor has a working range up to 400 g/m2·h. The statistical analysis and the Bland-Altman plot proved that our sensor is comparable to the medical device used as gold standard. At low sweat rate, the bias is 3.4 g/m2·h with a standard deviation of 7.6 g/m2·h. At maximum sweat rates, the bias is 2.3 g/m2·h with a standard deviation 6.9 g/m2·h. The p values for the Bland-Altman plots at low and maximum sweat rate (0.1331 and 0.2477 obtained by Kolmogorov-Smirnov test, respectively) allow the hypothesis that there is a significant difference between our sweat rate sensor and the medical device to be rejected. Conclusion We presented a prototype of a wearable sweat rate sensor for localized measurements. The trials on thirteen athletes proved that the performance of our sensor is comparable to that of a commercial medical device. This sweat rate sensor can provide valuable information on athletes' hydration status

Study of soft materials, flexible electronics, and machine learning for fully portable and wireless brain-machine interfaces

Over 300,000 individuals in the United States are afflicted with some form of limited motor function from brainstem or spinal-cord related injury resulting in quadriplegia or some form of locked-in syndrome. Conventional brain-machine interfaces used to allow for communication or movement require heavy, rigid components, uncomfortable headgear, excessive numbers of electrodes, and bulky electronics with long wires that result in greater data artifacts and generally inadequate performance. Wireless, wearable electroencephalograms, along with dry non-invasive electrodes can be utilized to allow recording of brain activity on a mobile subject to allow for unrestricted movement. Additionally, multilayer microfabricated flexible circuits, when combined with a soft materials platform allows for imperceptible wearable data acquisition electronics for long term recording. This dissertation aims to introduce new electronics and training paradigms for brain-machine interfaces to provide remedies in the form of communication and movement for these individuals. Here, training is optimized by generating a virtual environment from which a subject can achieve immersion using a VR headset in order to train and familiarize with the system. Advances in hardware and implementation of convolutional neural networks allow for rapid classification and low-latency target control. Integration of materials, mechanics, circuit and electrode design results in an optimized brain-machine interface allowing for rehabilitation and overall improved quality of life.Ph.D