Augmented reality and virtual reality technologies are increasing in popularity. Augmented reality has thrived to date mainly on mobile applications, with games like Pokémon Go or the new Google Maps utility as some of its ambassadors. On the other hand, virtual reality has been popularized mainly thanks to the videogame industry and cheaper devices. However, what was initially a failure in the industrial field is resurfacing in recent years thanks to the technological improvements in devices and processing hardware. In this work, an in-depth study of the different fields in which augmented and virtual reality have been used has been carried out. This study focuses on conducting a thorough scoping review focused on these new technologies, where the evolution of each of them during the last years in the most important categories and in the countries most involved in these technologies will be analyzed. Finally, we will analyze the future trend of these technologies and the areas in which it is necessary to investigate to further integrate these technologies into society.
Abnormal foot postures during gait are common sources of pain and pathologies of the lower limbs. Measurements of foot plantar pressures in both dynamic and static conditions can detect these abnormal foot postures and prevent possible pathologies. In this work, a plantar pressure measurement system is developed to identify areas with higher or lower pressure load. This system is composed of an embedded system placed in the insole and a user application. The instrumented insole consists of a low-power microcontroller, seven pressure sensors and a low-energy bluetooth module. The user application receives and shows the insole pressure information in real-time and, finally, provides information about the foot posture. In order to identify the different pressure states and obtain the final information of the study with greater accuracy, a Deep Learning neural network system has been integrated into the user application. The neural network can be trained using a stored dataset in order to obtain the classification results in real-time. Results prove that this system provides an accuracy over 90% using a training dataset of 3000+ steps from 6 different users.
Multisensory integration is commonly used in various robotic areas to collect more environmental information using different and complementary types of sensors. Neuromorphic engineers mimics biological systems behavior to improve systems performance in solving engineering problems with low power consumption. This work presents a neuromorphic sensory integration scenario for measuring the rotation frequency of a motor using an AER DVS128 retina chip (Dynamic Vision Sensor) and a stereo auditory system on a FPGA completely event-based. Both of them transmit information with Address-Event-Representation (AER). This integration system uses a new AER monitor hardware interface, based on a Spartan-6 FPGA that allows two operational modes: real-time (up to 5 Mevps through USB2.0) and data logger mode (up to 20Mevps for 33.5Mev stored in onboard DDR RAM). The sensory integration allows reducing prediction error of the rotation speed of the motor since audio processing offers a concrete range of rpm, while DVS can be much more accurate.
Lung cancer has the highest mortality rate in the world, twice as high as the second highest. On the other hand, pathologists are overworked and this is detrimental to the time spent on each patient, diagnostic turnaround time, and their success rate.In this work, we design, implement, and evaluate a diagnostic aid system for non-small cell lung cancer detection, using Deep Learning techniques.The classifier developed is based on Artificial Intelligence techniques, obtaining an automatic classification result between healthy, adenocarcinoma and squamous cell carcinoma, given an histopathological image from lung tissue. Moreover, a report module based on Explainable Deep Learning techniques is included and gives the pathologist information about the image's areas used to classify the sample and the confidence of belonging to each class.The results show a system accuracy between 97.11 and 99.69%, depending on the number of classes classified, and a value of the area under ROC curve between 99.77 and 99.94%.The classification results obtain a substantial improvement according to previous works. Thanks to the given report, the time spent by the pathologist and the diagnostic turnaround time can be reduced.
Falls have become a relevant public health issue due to their high prevalence and negative effects in elderly people. Wearable fall detector devices allow the implementation of continuous and ubiquitous monitoring systems. The effectiveness for analyzing temporal signals with low energy consumption is one of the most relevant characteristics of these devices. Recurrent neural networks (RNNs) have demonstrated a great accuracy in some problems that require analyzing sequential inputs. However, getting appropriate response times in low power microcontrollers remains a difficult task due to their limited hardware resources. This work shows a feasibility study about using RNN-based deep learning models to detect both falls and falls' risks in real time using accelerometer signals. The effectiveness of four different architectures was analyzed using the SisFall dataset at different frequencies. The resulting models were integrated into two different embedded systems to analyze the execution times and changes in the model effectiveness. Finally, a study of power consumption was carried out. A sensitivity of 88.2% and a specificity of 96.4% was obtained. The simplest models reached inference times lower than 34 ms, which implies the capability to detect fall events in real-time with high energy efficiency. This suggests that RNN models provide an effective method that can be implemented in low power microcontrollers for the creation of autonomous wearable fall detection systems in real-time.
In stereo-vision processing, the image-matching step is essential for results, although it involves a very high computational cost. Moreover, the more information is processed, the more time is spent by the matching algorithm, and the more inefficient it is. Spike-based processing is a relatively new approach that implements processing methods by manipulating spikes one by one at the time they are transmitted, like a human brain. The mammal nervous system can solve much more complex problems, such as visual recognition by manipulating neuron spikes. The spike-based philosophy for visual information processing based on the neuro-inspired address-event-representation (AER) is currently achieving very high performance. The aim of this work was to study the viability of a matching mechanism in stereo-vision systems, using AER codification and its implementation in a field-programmable gate array (FPGA). Some studies have been done before in an AER system with monitored data using a computer; however, this kind of mechanism has not been implemented directly on hardware. To this end, an epipolar geometry basis applied to AER systems was studied and implemented, with other restrictions, in order to achieve good results in a real-time scenario. The results and conclusions are shown, and the viability of its implementation is proven.
Abnormal foot postures can be measured during the march by plantar pressures in both dynamic and static conditions. These detections may prevent possible injuries to the lower limbs like fractures, ankle sprain or plantar fasciitis. This information can be obtained by an embedded instrumented insole with pressure sensors and a low-power microcontroller. However, these sensors are placed in sparse locations inside the insole, so it is not easy to correlate manually its values with the gait type; that is why a machine learning system is needed. In this work, we analyse the feasibility of integrating a machine learning classifier inside a low-power embedded system in order to obtain information from the user’s gait in real-time and prevent future injuries. Moreover, we analyse the execution times, the power consumption and the model effectiveness. The machine learning classifier is trained using an acquired dataset of 3000+ steps from 6 different users. Results prove that this system provides an accuracy over 99% and the power consumption tests obtains a battery autonomy over 25 days.
Since its emergence at the end of 2019, the pandemic caused by the COVID-19 virus has led to multiple changes in health protocols around the world [...]
The study and monitoring of wildlife and in semi-freedom has always been a subject of great interest. In recent years the technology allows to design low cost systems that facilitate these tasks: microcontrollers, low-power wireless networks, sensors. GPS, satellite and VHF has been used for position tracking and localization of wildlife. Our aim is to design a low-cost system for local monitoring of wildlife (collar or harness) with local memory and remote access. It will use multiples sensors for behavioral and health monitoring and fuse the processed information locally to reduce the stored data, but allowing to be sporadically transmitted through wireless networks. This collar will be based on an embedded low-power microcontroller with 802.15.4 transceiver and a set of sensors to provide data (activity and health) of the animal under monitoring: accelerometers, compass, humidity, temperature, light, microphone, heart rhythm. This collar is called mote in wireless sensors network (WSN) terminology. Only when one mote is closer to an 802.15.4 mote and under request, the collar will dump the information to a host database server through the 802.15.4 network. In this paper we present a viability study of the WSN for Donana Natural Park for different mote transmission powers, frequencies and distances for coverage. ZigBee and XBee mote has been proven.
