Tailored virtual reality and mobile application for motor rehabilitation

The present work presents a measurement system and methodology for hand and finger motor rehabilitation. The interaction with serious games developed in Unity 3D game engine is performed using a natural user interface based on Leap Motion Controller. The storage and management of data related to patient identification, established training plans and training results in LeaPhysio system, is realized in a client server architecture. The stored information can be accessed through a developed LeaPhysio App for Android OS platform, which also allows configuration of training plan by a therapist. Different metrics were included in the measurement system to provide to users the possibility to evaluate in an objective way the motor rehabilitation. The tests have shown that the developed system can provide accurate data on hand and finger movements in a meaningful and motivating exercise environment.info:eu-repo/semantics/acceptedVersio

Remote sensing technologies for physiotherapy assessment

The paper presents a set of remote, unobtrusive sensing technologies that can be used in upper and lower limbs rehabilitation monitoring. The advantages of using sensors based on microwave Doppler radar or infrared technologies for physiotherapy assessment are discussed. These technologies allow motion sensing at distance from monitored subject, reducing thus the discomfort produced by some wearable technologies for limbs movement assessment. The microwave radar that may be easily hidden into environment by nonmetallic parts allows remote sensing of human motion, providing information on user movements characteristics and patterns. The infrared technologies - infrared LEDs from Leap-Motion, infrared laser from Kinect depth sensor, and infrared thermography can be used for different movements' parameters evaluation. Visible for users, Leap-motion and Kinect sensors assure higher accuracy on body parts movements' detection at low computation load. These technologies are commonly used for virtual reality (VR) and augmented reality (AR) scenarios, in which the user motion patterns and the muscular activity might be analyzed. Thermography can be employed to evaluate the muscular loading. Muscular activity during movements training in physiotherapy can be estimated through skin temperature measurement before and after physical training. Issues related to the considered remote sensing technologies such as VR serious game for motor rehabilitation, signal processing and experimental results associated with microwave radar, infrared sensors and thermography for physiotherapy sensing are included in the paper.info:eu-repo/semantics/acceptedVersio

Smart sensing and AI for physical therapy in IoT era

It is well known that medical spending increase with disability status. Per capita spending for people with five or more limitations in activities of daily living (ADLs) is nearly five times the amount incurred by those with limitations in only one instrumental activities of daily living (IADLs). Physical therapy is the way to improve the motor capabilities however it takes a lot of time, it requires physiotherapists services, is often painful and the outcome are evaluated in subjective way. New technologies including smart sensors were adopted in healthcare including wearable solutions for cardiac and respiratory activity monitoring and successfully are contributing to reduce the costs of services. In the case of motor activity and particularly in physical rehabilitation the developments are still reduced the physical therapy services are using as hardware mechanical equipment without sensing, embedded processing and internet connectivity that significatively reduce the possibility to measure and evaluate the physical training outcomes in objective way. In this paper the disruptive solutions for physical therapy are presented that are based on hot technologies such as smart sensors, IoT, virtual reality (VR), mixed reality (MR), and artificial intelligence (AI). Applied AI may conduct to develop models, classifiers (gait classification) and short term or medium term prediction of physical therapy outcomes. Highly motivation of the patients under physical rehabilitation can be increased promoting serious game characterized by VR and MR scenariosinfo:eu-repo/semantics/publishedVersio

Wrist and hand rehabilitation software platform based on leap motion controller

A software platform based on Leap Motion Controller (LMC) movements’ detection was developed. It allows measurements of clinically proved effective hand and finger exercises. The developed software allows representation of amplitude of each different movement, time interval for each movement, frequency of different movements, asymmetry of bilateral movements, standard deviation of signal amplitude, Poincaré plots. A serious game Collect Color Cube, was developed using Unity, C# scrips, and signals from LMC related to movements of the user’s hands and fingers.info:eu-repo/semantics/publishedVersio

Multimodal approach for emotion recognition based on simulated flight experiments

The present work tries to fill part of the gap regarding the pilots' emotions and their bio-reactions during some flight procedures such as, takeoff, climbing, cruising, descent, initial approach, final approach and landing. A sensing architecture and a set of experiments were developed, associating it to several simulated flights ( N f l i g h t s = 13 ) using the Microsoft Flight Simulator Steam Edition (FSX-SE). The approach was carried out with eight beginner users on the flight simulator ( N p i l o t s = 8 ). It is shown that it is possible to recognize emotions from different pilots in flight, combining their present and previous emotions. The cardiac system based on Heart Rate (HR), Galvanic Skin Response (GSR) and Electroencephalography (EEG), were used to extract emotions, as well as the intensities of emotions detected from the pilot face. We also considered five main emotions: happy, sad, angry, surprise and scared. The emotion recognition is based on Artificial Neural Networks and Deep Learning techniques. The Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were the main methods used to measure the quality of the regression output models. The tests of the produced output models showed that the lowest recognition errors were reached when all data were considered or when the GSR datasets were omitted from the model training. It also showed that the emotion surprised was the easiest to recognize, having a mean RMSE of 0.13 and mean MAE of 0.01; while the emotion sad was the hardest to recognize, having a mean RMSE of 0.82 and mean MAE of 0.08. When we considered only the higher emotion intensities by time, the most matches accuracies were between 55% and 100%.info:eu-repo/semantics/publishedVersio

β-band analysis from simulated flight experiments

Several safety-related improvements are applied every year to try to minimize the total number of civil aviation accidents. Fortunately, these improvements work well, reducing the number of accident occurrences. However, while the number of accidents due to mechanical failures has decreased, the number of accidents due to human errors seems to grow. On that basis, this work presents a contribution regarding the brain’s β-band activities for different levels of volunteers’ expertise on flight simulator, i.e., experienced, mid-level and beginner, in which they acted as pilots in command during several simulated flights. Spectrogram analysis and statistical measurements of each volunteer’s brain’s β-band were carried out. These were based on seven flight tasks: takeoff, climb, cruise flight, descent, approach, final approach and landing. The results of the proposed experiment showed that the takeoff, approach and landing corresponded to the highest brain activities, i.e., close to 37.06–67.33% more than the brain activity of the other flight tasks: when some accidents were about to occur, the intensities of the brain activity were similar to those of the final approach task. When the volunteers’ expertise and confidence on flight simulation were considered, it was shown that the highest brain magnitudes and oscillations observed of more experienced and confident volunteers were on average close to 68.44% less, compared to less experienced and less confident volunteers. Moreover, more experienced and confident volunteers in general presented different patterns of brain activities compared to volunteers with less expertise or less familiarity with fight simulations and/or electronic games.info:eu-repo/semantics/publishedVersio

IoT-based systems for soil nutrients assessment in horticulture

Soil nutrients assessment has great importance in horticulture. Implementation of an information system for horticulture faces many challenges: (i) great spatial variability within farms (e.g., hilly topography); (ii) different soil properties (e.g., different water holding capacity, different content in sand, sit, clay, and soil organic matter, different pH, and different permeability) for different cultivated plants; (iii) different soil nutrient uptake by different cultivated plants; (iv) small size of monoculture; and (v) great variety of farm components, agroecological zone, and socio-economic factors. Advances in information and communication technologies enable creation of low cost, efficient information systems that would improve resources management and increase productivity and sustainability of horticultural farms. We present an information system based on different sensing capability, Internet of Things, and mobile application for horticultural farms. An overview on different techniques and technologies for soil fertility evaluation is also presented. The results obtained in a botanical garden that simulates the diversity of environment and plant diversity of a horticultural farm are discussed considering the challenges identified in the literature and field research. The study provides a theoretical basis and technical support for the development of technologies that enable horticultural farmers to improve resources management.info:eu-repo/semantics/publishedVersio

Physical rehabilitation based on kinect serious games

This article presents a serious game framework developed using Unity 3D game engine and Kinect V2 sensor as a natural user interface. The developed serious games are used for objective evaluation of physical rehabilitation considering the Kinect V2 sensors for 3D motion detection of different body joints training and provide different types of data, such as angles velocities, for physiotherapists and patients during the rehabilitation process. The framework provide data storage capability in a remote database thus patient's biometric data, patients' medical record, obtained scores during serious game based training and values of metrics such as the distance between feet during game, left right feet usage frequency and execution time for imposed movement associated with game mechanics. A general description of the applied technologies on serious game for lower limb rehabilitation developments as so as the experimental results obtained for a set of volunteers are included in the paper.info:eu-repo/semantics/acceptedVersio

Exergames for motor rehabilitation in older adults: an umbrella review

Background: Exergames have been used as an innovative motor rehabilitation method with the main aim of improving motivation and exercise. As research interest in exergaming for rehabilitation is rapidly growing, a review of existing systematic reviews is important to synthesize the available evidence and provide recommendations. Objectives: In this article, we systematically synthesized the information from reviews that have examined the effects if exergames on different body movement parameters in older adults with and without specific pathologies. Method: Searches were conducted in Web of Science, Scopus, PsycARTICLES, PsycINFO, Psychology and Behavioural Sciences Collection, PubMed, SciELO, B-On and Google Scholar, articulating different terms and Boolean operators. Systematic reviews, meta-analysis and literature reviews published until May 2017 that investigated exergame interventions on physical outcomes, such as balance, gait, limb movements, muscle strength, in healthy and non-healthy older adults. Results: Based on prior reviews, exergaming, as a standalone intervention, has a positive effect on balance, gait, muscle strength, upper limb function, and dexterity. When compared to traditional physiotherapy, exergaming has at least similar effects on these outcomes. Many of the investigated studies indicated low methodological quality for the evaluation of the effects of exergames on different outcomes related to motor rehabilitation. Conclusions: Exergames could be used as a complement to traditional forms of motor rehabilitation, but future individual studies and reviews should follow more rigorous methodological standards in order to improve the quality of the evidence and provide guidelines for the use of exergames in motor rehabilitation.info:eu-repo/semantics/acceptedVersio

Physiological and behavior monitoring systems for smart healthcare environments: a review

Healthcare optimization has become increasingly important in the current era, where numerous challenges are posed by population ageing phenomena and the demand for higher quality of the healthcare services. The implementation of Internet of Things (IoT) in the healthcare ecosystem has been one of the best solutions to address these challenges and therefore to prevent and diagnose possible health impairments in people. The remote monitoring of environmental parameters and how they can cause or mediate any disease, and the monitoring of human daily activities and physiological parameters are among the vast applications of IoT in healthcare, which has brought extensive attention of academia and industry. Assisted and smart tailored environments are possible with the implementation of such technologies that bring personal healthcare to any individual, while living in their preferred environments. In this paper we address several requirements for the development of such environments, namely the deployment of physiological signs monitoring systems, daily activity recognition techniques, as well as indoor air quality monitoring solutions. The machine learning methods that are most used in the literature for activity recognition and body motion analysis are also referred. Furthermore, the importance of physical and cognitive training of the elderly population through the implementation of exergames and immersive environments is also addressedinfo:eu-repo/semantics/publishedVersio