Simultaneous Bayesian recognition of locomotion and gait phases with wearable sensors

Recognition of movement is a crucial process to assist humans in activities of daily living, such as walking. In this work, a high-level method for the simultaneous recognition of locomotion and gait phases using wearable sensors is presented. A Bayesian formulation is employed to iteratively accumulate evidence to reduce uncertainty, and to improve the recognition accuracy. This process uses a sequential analysis method to autonomously make decisions, whenever the recognition system perceives that there is enough evidence accumulated. We use data from three wearable sensors, attached to the thigh, shank, and foot of healthy humans. Level-ground walking, ramp ascent and descent activities are used for data collection and recognition. In addition, an approach for segmentation of the gait cycle for recognition of stance and swing phases is presented. Validation results show that the simultaneous Bayesian recognition method is capable to recognize walking activities and gait phases with mean accuracies of 99.87% and 99.20%. This process requires a mean of 25 and 13 sensor samples to make a decision for locomotion mode and gait phases, respectively. The recognition process is analyzed using different levels of confidence to show that our method is highly accurate, fast, and adaptable to specific requirements of accuracy and speed. Overall, the simultaneous Bayesian recognition method demonstrates its benefits for recognition using wearable sensors, which can be employed to provide reliable assistance to humans in their walking activities

Probabilistic locomotion mode recognition with wearable sensors

Recognition of locomotion mode is a crucial process for control of wearable soft robotic devices to assist humans in walking activities. We present a probabilistic Bayesian approach with a sequential analysis method for recognition of locomotion and phases of the gait cycle. Our approach uses recursive accumulation of evidence, as biological systems do, to reduce uncertainty present in the sensor measurements, and thus improving recognition accuracy. Data were collected from a wearable sensor, attached to the shank of healthy human participants, from three locomotion modes; level-ground walking, ramp ascent and ramp descent. We validated our probabilistic approach with recognition of locomotion in steady-state and gait phases in transitional states. Furthermore, we evaluated the effect, in recognition accuracy, of the accumulation of evidence controlled by increasing belief thresholds. High accuracy results achieved by our approach, demonstrate its potential for robust control of lower limb wearable soft robotic devices to provide natural and safe walking assistance to humans

Prediction of gait events in walking activities with a Bayesian perception system

In this paper, a robust probabilistic formulation for prediction of gait events from human walking activities using wearable sensors is presented. This approach combines the output from a Bayesian perception system with observations from actions and decisions made over time. The perception system makes decisions about the current gait events, while observations from decisions and actions allow to predict the most probable gait event during walking activities. Furthermore, our proposed method is capable to evaluate the accuracy of its predictions, which permits to obtain a better performance and trade-off between accuracy and speed. In our work, we use data from wearable inertial measurement sensors attached to the thigh, shank and foot of human participants. The proposed perception system is validated with multiple experiments for recognition and prediction of gait events using angular velocity data from three walking activities; level-ground, ramp ascent and ramp descent. The results show that our method is fast, accurate and capable to evaluate and adapt its own performance. Overall, our Bayesian perception system demonstrates to be a suitable high-level method for the development of reliable and intelligent assistive and rehabilitation robots

A combined Adaptive Neuro-Fuzzy and Bayesian strategy for recognition and prediction of gait events using wearable sensors

A robust strategy for recognition and prediction of gait events using wearable sensors is presented in this paper. The strategy adopted here uses a combination of two computational intelligence approaches: Adaptive Neuro-Fuzzy and Bayesian methods. Recognition of gait events is performed by a Bayesian method which iteratively accumulates evidence to reduce uncertainty from sensor measurements. Prediction of gait events is based on the observation of decisions and actions made over time by our perception system. An Adaptive Neuro-Fuzzy system evaluates the reliability of predictions, learns a weighting parameter and controls the amount of predicted information to be used by our Bayesian method. Thus, this strategy ensures the achievement of better recognition and prediction performance in both accuracy and speed. The methods are validated with experiments for recognition and prediction of gait events with different walking activities, using data from wearable sensors attached to lower limbs of participants. Overall, results show the benefits of our combined Adaptive Neuro-Fuzzy and Bayesian strategy to achieve fast and accurate decisions, but also to evaluate and adapt its own performance, making it suitable for the development of intelligent assistive and rehabilitation robots

Recyclable supported Pd-NHC catalytic systems for the copper free Sonogashira cross-coupling in flow

A new family of well-defined NHC-Pd complexes immobilised onto silica, alumina and titania is reported. The catalysts display activity and recyclability in the Sonogashira cross-coupling reactions under batch and continuous flow conditions. Under batch conditions the new catalytic systems were recycled up to four times with yields over 80%. These catalysts have a broad substrate scope of different aryl bromides and alkynes. The titania-supported catalysts show good conversions under continuous flow conditions at least over 8 hours on stream using MeOH as solvent.The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (EC FP7) Grant Agreement n. [NMP2-SL-2012-280827]. The authors are grateful to TECSPR14-1-0045; TECNIOspring programme, the Agency for Business Competitiveness of the Government of Catalonia, ACCIÓ, the Generalitat de Catalunya (2014SGR670). and the Spanish Ministerio de Economía y Competitividad (CTQ2016-75016-R) for financial support

Adaptive Bayesian inference system for recognition of walking activities and prediction of gait events using wearable sensors

In this paper, a novel approach for recognition of walking activities and gait events with wearable sensors is presented. This approach, called adaptive Bayesian inference system (BasIS), uses a probabilistic formulation with a sequential analysis method, for recognition of walking activities performed by participants. Recognition of gait events, needed to identify the state of the human body during the walking activity, is also provided by the proposed method. In addition, the BasIS system includes an adaptive action-perception method for the prediction of gait events. The adaptive approach uses the knowledge gained from decisions made over time by the inference system. The actionperception method allows the BasIS system to autonomously adapt its performance, based on the evaluation of its own predictions and decisions made over time. The proposed approach is implemented in a layered architecture and validated with the recognition of three walking activities; level-ground, ramp ascent and ramp descent. The validation process employs real data from three inertial measurements units attached to the thigh, shanks and foot of participants while performing walking activities. The experiments show that mean decision times of 240 ms and 40 ms are needed to achieve mean accuracies of 99.87% and 99.82% for recognition of walking activities and gait events, respectively. The validation experiments also show that the performance, in accuracy and speed, is not significantly affected when noise is added to sensor measurements. These results show that the proposed adaptive recognition system is accurate, fast and robust to sensor noise, but also capable to adapt its own performance over time. Overall, the adaptive BasIS system demonstrates to be a robust and suitable computational approach for the intelligent recognition of activities of daily living using wearable sensors

Genome-wide transcription analysis of clinal genetic variation in Drosophila.

Clinal variation in quantitative traits is widespread, but its genetic basis awaits identification. Drosophila melanogaster shows adaptive, clinal variation in traits such as body size along latitudinal gradients on multiple continents. To investigate genome wide transcription differentiation between North and South that might contribute to the clinal phenotypic variation, we compared RNA expression patterns during development of D. melanogaster from tropical northern and temperate southern populations using whole genome tiling arrays. We found that genes that were differentially expressed between the cline ends were generally associated with metabolism and growth, and experimental alteration of expression of a sample of them generally resulted in altered body size in the predicted direction, sometimes significantly so. We further identified the serpent (srp) transcription factor binding sites to be enriched near genes up-regulated in expression in the south. Analysis of clinal populations revealed a significant cline in the expression level of srp. Experimental over-expression of srp increased body size, as predicted from its clinal expression pattern, suggesting that it may be involved in regulating adaptive clinal variation in Drosophila. This study identified a handful of genes that contributed to clinal phenotypic variation through altered gene expression level, yet misexpression of individual gene led to modest body size change

The smoking habits, attitudes towards smoking and knowledge regarding anti-smoking legislation of students in institutions of higher learning in the Eastern Cape Province of South Africa

Background : The study aimed to i) investigate the smoking habits of students attending tertiary institutions of learning in the Eastern Cape Province (ECP) and ii) determine the knowledge of the students about the health hazards of smoking and their attitude towards current government anti-smoking legislation. Methods: This was a questionnaire-based study involving all seven tertiary institutions of learning in the ECP, viz. the Universities of Transkei, Fort Hare, Port Elizabeth and Rhodes University, and the Border, Eastern Cape and Port Elizabeth technikons. A total of 1 728 students were interviewed out of a student population of 30,080. Stratified random sampling was used to select the students. Two-way tables were used to test the independence of the variables and chi-square tests were applied. A ‘p' value of below 5% was used as a test of significance. Results: A total of 1 480 students completed the questionnaires (86%). The racial classification of the respondents was 79% Black, 13% White, 7% Coloured and 2% Indian. Twenty-six per cent of the students were smokers, of which 37% were male and 15% were female. Forty-five per cent of the Coloured students smoked, while the figures for Whites and Blacks were 26% and 25% respectively. Seventy per cent of the students smoked less than 10 cigarettes a day. Fifty-two per cent of the smokers said they wanted to stop smoking. Sixty-one per cent had been influenced to start smoking by their friends and only 13% were influenced by advertisements. Ninety-four percent agreed that smoking was dangerous to the smoker's health, while 73% responded that there was a relationship between mothers who smoke and low birth weight. Thirteen per cent thought the legislation was too tough, while 30% said it was good as it was. On measures to reduce smoking, 86% favoured restricting smoking in public places. Conclusion: This study has demonstrated that smoking is prevalent in tertiary institutions of learning in the Eastern Cape Province. The demographic profile of the smokers reflects the national picture. The knowledge of the harmful effects of smoking is generally good. For full text, click here: South African Family Practice 2006; Vol.48(9):14-14

Walking Activity Recognition with sEMG Sensor Array on Thigh Circumference using Convolutional Neural Network

In recognition of walking gait modes using surface electromyography (sEMG), the use of sEMG sensor array can provide sensor redundancy and less rigorous identification of sEMG electrode placements as compared to the conventional sEMG electrode placements right in the middle of muscle bellies. However, the potentially lesser discriminative and noisier sEMG signals from the sEMG sensor array pose the challenge in developing accurate and robust machine learning classifier for walking activity recognition. In this paper, we explore the use of convolution neural network (CNN) classifier with frequency gradient feature derived from EMG signal spectrogram for detecting different walking activities using an sEMG sensor array on thigh circumference. EMG dataset from five healthy subjects and an amputee for five walking activities namely walking at slow, normal and fast speed, ramp ascending and ramp descending are used to train and test the CNN-based classifier. Our preliminary findings suggest that frequency gradient feature can improve the CNN-based classifier performance for walking activity recognition using EMG sensor array on thigh circumference

A model identification approach to quantify impact of whole-body vertical vibrations on limb compliant dynamics and walking stability

Extensive research is ongoing in the field of orthoses/exoskeleton design for efficient lower limbs assistance. However, despite wearable devices reported to improve lower limb mobility, their structural impacts on whole-body vertical dynamics have not been investigated. This study introduced a model identification approach and frequency domain analysis to quantify the impacts of orthosis-generated vibrations on limb stability and contractile dynamics. Experiments were recorded in the motion capture lab using 11 unimpaired subjects by wearing an adjustable ankle–foot orthosis (AFO). The lower limb musculoskeletal structure was identified as spring-mass (SM) and spring-mass-damper (SMD) based compliant models using the whole-body centre-of-mass acceleration data. Furthermore, Nyquist and Bode methods were implemented to quantify stabilities resulting from vertical impacts. Our results illustrated a significant decrease (p < 0.05) in lower limb contractile properties by wearing AFO compared with a normal walk. Also, stability margins quantified by wearing AFO illustrated a significant variance in terms of gain-margins (p < 0.05) for both loading and unloading phases whereas phase-margins decreased (p < 0.05) only for the respective unloading phases. The methods introduced here provide evidence that wearable orthoses significantly affect lower limb vertical dynamics and should be considered when evaluating orthosis/prosthesis/exoskeleton effectiveness