Fall Prediction and Prevention Systems: Recent Trends, Challenges, and Future Research Directions.

Fall prediction is a multifaceted problem that involves complex interactions between physiological, behavioral, and environmental factors. Existing fall detection and prediction systems mainly focus on physiological factors such as gait, vision, and cognition, and do not address the multifactorial nature of falls. In addition, these systems lack efficient user interfaces and feedback for preventing future falls. Recent advances in internet of things (IoT) and mobile technologies offer ample opportunities for integrating contextual information about patient behavior and environment along with physiological health data for predicting falls. This article reviews the state-of-the-art in fall detection and prediction systems. It also describes the challenges, limitations, and future directions in the design and implementation of effective fall prediction and prevention systems

Investigating the Effects of Custom Made Orthotics on Brain Forms: A Pilot Study

OBJECTIVES: To determine (1) the feasibility of this novel approach and technique of recording brain activity, wirelessly and continuously, during human gait, and (2) if custom made orthotics will alter the brain activity patterns recorded. METHODS: Gait trials were performed on 16 participants walking with and without orthotic devices in their shoes while simultaneously collecting EEG data through the Emotiv wireless neuroheadset. RESULTS: The Emotiv neuroheadset was capable of detecting changes in brain activity between the two gait trials. The differences in brain activity identified between conditions were not statistically significant. CONCLUSION: The findings suggest the Emotiv EEG device is sensitive enough to detect changes in brain activation patterns during human gait. Further research is required before definite conclusions can be made about this novel device, or about what effects, if any, orthotics have on brain activation patterns during gait

Southwest Research Institute assistance to NASA in biomedical areas of the technology utilization program Final report, 1 Feb. 1969 - 24 Aug. 1970

Research progress in technology transfer by NASA Biomedical Application Tea

Brain activity on encoding different textures EEG signal acquisition with ExoAtlet®

Powered exoskeletons play a crucial role in the rehabilitation field improving the quality of life for those who need them. Thus, being a major contribution for patients integration into society, providing them with more autonomy and freedom. In spite of these positive outcomes, a thorough description of the brain correlates connected to exoskeleton control is still needed. For instance, the perception of different pavement textures when wearing an exoskeleton is probably going to cause changes in cerebral activity, which could impact both sensory encoding and Brain-Computer Interface (BCI) control. Therefore, the main goal of this work is to describe the brain activity response to different textured pavements using ExoAtlet ® powered exoskeleton. In order to measure, process, analyze and classify the impact of different textures on neurophysiological rhythms, 4-minute signals were recorded by Electroencephalogram (EEG) with a 16-channel cap (actiCAP by Brain Products). Each of the three experimental subjects was instructed to walk in place on four different types of pavement (flat, carpet, foam, and rubber circles) with and without the exoskeleton, for a total of eight different experimental conditions. A counterbalanced design was applied, and informed consent was obtained from participants (Committee for Health Sciences of the Universidade Católica Portuguesa - 99/2022). Additionally, four machine learning methods, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), and Artificial Neural Network (ANN), were selected in order to analyze three distinct classification problems. This study found that there were changes associated with the delta frequency band for electrodes C3 and C4, and when comparing the classifiers performance, LDA presented the best accuracy across the three classification problems involving all subjects. Thereby, this work concludes that the results are consistent with the hypothesis that sensory processing of pavement textures during exoskeleton control induces neural changes and delta variations of the C3 and C4 electrodes. Additionally, LDA demonstrated the best performance across the three classifications of subject-independent problems.Os exoesqueletos motorizados desempenham um papel crucial no campo da reabilitação, melhorando a qualidade de vida das pessoas que deles necessitam. Deste modo, são um contributo importante para que os pacientes com condições físicas limitadas sejam mais facilmente integrados na sociedade, proporcionando-lhes mais autonomia e liberdade. Embora esta tecnologia tenha os seus aspetos positivos, ainda existe a necessidade de descrever os correlatos cerebrais direcionados para o controlo do exoesqueleto. Por exemplo, a percepção de diferentes pavimentos quando se usa um exoesqueleto vai provavelmente causar alterações na actividade cerebral, o que pode ter impacto tanto na codificação sensorial como no controlo da interface cérebro-máquina (BCI). Deste modo, o principal objetivo deste trabalho é descrever a atividade cerebral às diferentes texturas dos pavimentos, utilizando o exoesqueleto ExoAtlet ®. A fim de medir, processar, analisar e classificar o impacto de diferentes texturas em ritmos neurofisiológicos, foram registados sinais de 4 minutos atravês the Eletroencefalograma (EEG) com uma touca de 16 canais (actiCAP by Brain Products). Cada um dos três voluntários foi instruído a dar passos no lugar em quatro tipos diferentes de pavimento (plano, alcatifa, espuma, e círculos de borracha) com e sem o exosqueleto, num total de oito condições experimentais diferentes. Foi aplicado um desenho contrabalançado e foi obtido o consentimento informado dos participantes (Comissão para as Ciências da Saúde da Universidade Católica Portuguesa - 99/2022). Adicionalmente, foram selecionados quatro classificadores: máquinas de vetores de suporte (SVM), k-vizinhos mais próximos (KNN), análise discriminante linear (LDA) e redes neuronais artificiais (ANN) para analisar três problemas de classificação distintos. Os resultados obtidos por este estudo demonstraram que existiam alterações associadas à banda de frequência delta para os eléctrodos C3 e C4 e, ao comparar o desempenho dos classificadores, o LDA apresentou a melhor exatidão nos três problemas de classificação envolvendo todos os sujeitos. Assim, estes resultados são consistentes com a hipótese de que o processamento sensorial dos pavimentos durante o controlo do exoesqueleto induz alterações neuronais

Change blindness: eradication of gestalt strategies

Arrays of eight, texture-defined rectangles were used as stimuli in a one-shot change blindness (CB) task where there was a 50% chance that one rectangle would change orientation between two successive presentations separated by an interval. CB was eliminated by cueing the target rectangle in the first stimulus, reduced by cueing in the interval and unaffected by cueing in the second presentation. This supports the idea that a representation was formed that persisted through the interval before being 'overwritten' by the second presentation (Landman et al, 2003 Vision Research 43149–164]. Another possibility is that participants used some kind of grouping or Gestalt strategy. To test this we changed the spatial position of the rectangles in the second presentation by shifting them along imaginary spokes (by ±1 degree) emanating from the central fixation point. There was no significant difference seen in performance between this and the standard task [F(1,4)=2.565, p=0.185]. This may suggest two things: (i) Gestalt grouping is not used as a strategy in these tasks, and (ii) it gives further weight to the argument that objects may be stored and retrieved from a pre-attentional store during this task

Critical evaluation and novel design of a non-invasive and wearable health monitoring system

This thesis was submitted for the degree of Master of Philosophy and awarded by Brunel University.This study is about developing a non-invasive wearable health-monitoring system. The project aims to achieve miniaturisation as much as possible, using nanotechnology. The achieved results of the project are nothing but conceptual images of a convertible watch. The system is a non-invasive health measurement system. An important part of the study is researching the automation of blood pressure measurement by means of experiments which test the effect of exterior factors on blood pressure level. These experiments have been held to improve the automation and simplicity of BP measurements to establish a 24hr BP monitoring system. This study proposed a medical sensor that is part of the watch system, and that is most compatible with the elderly people product preferences in the UK. The “sensor strip” is in cm range, integrating a number of MEMS sensors, for the non-invasive detection of certain health aspects. The health aspects are chosen according to how close they are from the “health vital signs”, which are the first measurements executed by the doctor, if a patient is to visit him. An applied QFD study showed that the most suitable measurement technology to be used in the proposed sensor strip is the infrared technology. In addition to the sensor strip, EEG health detection is added, which is the reason why the watch is convertible. MEMS sensors, MEMS memory and an embedded processor are selected, since that this project also includes minimising the size of a device where the utilization of nanotechnology is vital. The final result of the study is only a conceptual design of a product with a carefully selected subsystems. The software design of the product will not be further developed to become a physical prototype of a consumer product

Technology applications

A summary of NASA Technology Utilization programs for the period of 1 December 1971 through 31 May 1972 is presented. An abbreviated description of the overall Technology Utilization Applications Program is provided as a background for the specific applications examples. Subjects discussed are in the broad headings of: (1) cancer, (2) cardiovascular disease, (2) medical instrumentation, (4) urinary system disorders, (5) rehabilitation medicine, (6) air and water pollution, (7) housing and urban construction, (8) fire safety, (9) law enforcement and criminalistics, (10) transportation, and (11) mine safety

Volitional Control of Lower-limb Prosthesis with Vision-assisted Environmental Awareness

Early and reliable prediction of user’s intention to change locomotion mode or speed is critical for a smooth and natural lower limb prosthesis. Meanwhile, incorporation of explicit environmental feedback can facilitate context aware intelligent prosthesis which allows seamless operation in a variety of gait demands. This dissertation introduces environmental awareness through computer vision and enables early and accurate prediction of intention to start, stop or change speeds while walking. Electromyography (EMG), Electroencephalography (EEG), Inertial Measurement Unit (IMU), and Ground Reaction Force (GRF) sensors were used to predict intention to start, stop or increase walking speed. Furthermore, it was investigated whether external emotional music stimuli could enhance the predictive capability of intention prediction methodologies. Application of advanced machine learning and signal processing techniques on pre-movement EEG resulted in an intention prediction system with low latency, high sensitivity and low false positive detection. Affective analysis of EEG suggested that happy music stimuli significantly (