| 1. |
- Bixby, H., et al.
(author)
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Rising rural body-mass index is the main driver of the global obesity epidemic in adults
- 2019
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In: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 569:7755, s. 260-4
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Journal article (peer-reviewed)abstract
- Body-mass index (BMI) has increased steadily in most countries in parallel with a rise in the proportion of the population who live in cities(.)(1,2) This has led to a widely reported view that urbanization is one of the most important drivers of the global rise in obesity(3-6). Here we use 2,009 population-based studies, with measurements of height and weight in more than 112 million adults, to report national, regional and global trends in mean BMI segregated by place of residence (a rural or urban area) from 1985 to 2017. We show that, contrary to the dominant paradigm, more than 55% of the global rise in mean BMI from 1985 to 2017-and more than 80% in some low- and middle-income regions-was due to increases in BMI in rural areas. This large contribution stems from the fact that, with the exception of women in sub-Saharan Africa, BMI is increasing at the same rate or faster in rural areas than in cities in low- and middle-income regions. These trends have in turn resulted in a closing-and in some countries reversal-of the gap in BMI between urban and rural areas in low- and middle-income countries, especially for women. In high-income and industrialized countries, we noted a persistently higher rural BMI, especially for women. There is an urgent need for an integrated approach to rural nutrition that enhances financial and physical access to healthy foods, to avoid replacing the rural undernutrition disadvantage in poor countries with a more general malnutrition disadvantage that entails excessive consumption of low-quality calories.
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| 2. |
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| 3. |
- Hemeren, Paul, et al.
(author)
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Kinematic-based classification of social gestures and grasping by humans and machine learning techniques
- 2021
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In: Frontiers in Robotics and AI. - : Frontiers Media S.A.. - 2296-9144. ; 8:308, s. 1-17
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Journal article (peer-reviewed)abstract
- The affective motion of humans conveys messages that other humans perceive and understand without conventional linguistic processing. This ability to classify human movement into meaningful gestures or segments plays also a critical role in creating social interaction between humans and robots. In the research presented here, grasping and social gesture recognition by humans and four machine learning techniques (k-Nearest Neighbor, Locality-Sensitive Hashing Forest, Random Forest and Support Vector Machine) is assessed by using human classification data as a reference for evaluating the classification performance of machine learning techniques for thirty hand/arm gestures. The gestures are rated according to the extent of grasping motion on one task and the extent to which the same gestures are perceived as social according to another task. The results indicate that humans clearly rate differently according to the two different tasks. The machine learning techniques provide a similar classification of the actions according to grasping kinematics and social quality. Furthermore, there is a strong association between gesture kinematics and judgments of grasping and the social quality of the hand/arm gestures. Our results support previous research on intention-from-movement understanding that demonstrates the reliance on kinematic information for perceiving the social aspects and intentions in different grasping actions as well as communicative point-light actions.
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| 4. |
- Li, Cai, et al.
(author)
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A novel approach to locomotion learning: Actor-Critic architecture using central pattern generators and dynamic motor primitives
- 2014
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In: Frontiers in Neurorobotics. - : Frontiers. - 1662-5218. ; 8
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Journal article (peer-reviewed)abstract
- In this article, we propose an architecture of a bio-inspired controller that addresses the problem of learning different locomotion gaits for different robot morphologies. The modeling objective is split into two: baseline motion modeling and dynamics adaptation. Baseline motion modeling aims to achieve fundamental functions of a certain type of locomotion and dynamics adaptation provides a "reshaping" function for adapting the baseline motion to desired motion. Based on this assumption, a three-layer architecture is developed using central pattern generators (CPGs, a bio-inspired locomotor center for the baseline motion) and dynamic motor primitives (DMPs, a model with universal "reshaping" functions). In this article, we use this architecture with the actor-critic algorithms for finding a good "reshaping" function. In order to demonstrate the learning power of the actor-critic based architecture, we tested it on two experiments: (1) learning to crawl on a humanoid and, (2) learning to gallop on a puppy robot. Two types of actor-critic algorithms (policy search and policy gradient) are compared in order to evaluate the advantages and disadvantages of different actor-critic based learning algorithms for different morphologies. Finally, based on the analysis of the experimental results, a generic view/architecture for locomotion learning is discussed in the conclusion.
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| 6. |
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| 7. |
- Li, Cai, et al.
(author)
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Humanoids learning to walk : a natural CPG-actor-critic architecture
- 2013
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In: Frontiers in Neurorobotics. - : Frontiers Media S.A.. - 1662-5218. ; 7:5
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Journal article (peer-reviewed)abstract
- The identification of learning mechanisms for locomotion has been the subject of much research for some time but many challenges remain. Dynamic systems theory (DST) offers a novel approach to humanoid learning through environmental interaction. Reinforcement learning (RL) has offered a promising method to adaptively link the dynamic system to the environment it interacts with via a reward-based value system. In this paper, we propose a model that integrates the above perspectives and applies it to the case of a humanoid (NAO) robot learning to walk the ability of which emerges from its value-based interaction with the environment. In the model, a simplified central pattern generator (CPG) architecture inspired by neuroscientific research and DST is integrated with an actor-critic approach to RL (cpg-actor-critic). In the cpg-actor-critic architecture, least-square-temporal-difference based learning converges to the optimal solution quickly by using natural gradient learning and balancing exploration and exploitation. Futhermore, rather than using a traditional (designer-specified) reward it uses a dynamic value function as a stability indicator that adapts to the environment. The results obtained are analyzed using a novel DST-based embodied cognition approach. Learning to walk, from this perspective, is a process of integrating levels of sensorimotor activity and value.
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| 8. |
- Li, Cai, et al.
(author)
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Humanoids that crawl : Comparing gait performance of iCub and NAO using a CPG architecture
- 2011
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In: Proceedings - 2011 IEEE International Conference on Computer Science and Automation Engineering, CSAE 2011. - : IEEE conference proceedings. - 9781424487271 - 9781424487288 - 9781424487264 - 9781424487257 ; , s. 577-582
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Conference paper (peer-reviewed)abstract
- In this article, a generic CPG architecture is used to model infant crawling gaits and is implemented on the NAO robot platform. The CPG architecture is chosen via a systematic approach to designing CPG networks on the basis of group theory and dynamic systems theory. The NAO robot performance is compared to the iCub robot which has a different anatomical structure. Finally, the comparison of performance and NAO whole-body stability are assessed to show the adaptive property of the CPG architecture and the extent of its ability to transfer to different robot morphologies. © 2011 IEEE.
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| 9. |
- Li, Cai, et al.
(author)
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k-Nearest-Neighbour based Numerical Hand Posture Recognition using a Smart Textile Glove
- 2015
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In: AMBIENT 2015. - : International Academy, Research and Industry Association (IARIA). - 9781612084213 ; , s. 36-41
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Conference paper (peer-reviewed)abstract
- In this article, the authors present an interdisciplinary project that illustrates the potential and challenges in dealing with electronic textiles as sensing devices. An interactive system consisting of a knitted sensor glove and electronic circuit and a numeric hand posture recognition algorithm based on k-nearestneighbour (kNN) is introduced. The design of the sensor glove itself is described, considering two sensitive fiber materials – piezoresistive and piezoelectric fibers – and the construction using an industrial knitting machine as well as the electronic setup is sketched out. Based on the characteristics of the textile sensors, a kNN technique based on a condensed dataset has been chosen to recognize hand postures indicating numbers from one to five from the sensor data. The authors describe two types of data condensation techniques (Reduced Nearest Neighbours and Fast Condensed Nearest Neighbours) in order to improve the data quality used by kNN, which are compared in terms of run time, condensation rate and recognition accuracy. Finally, the article gives an outlook on potential application scenarios for sensor gloves in pervasive computing.
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| 10. |
- Li, Cai, et al.
(author)
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Modelling Walking Behaviors Based on CPGs : A Simplified Bio-inspired Architecture
- 2012
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In: From Animals to Animats 12. - Berlin; Heidelberg : Springer Berlin/Heidelberg. - 9783642330926 - 9783642330933 ; , s. 156-166
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Conference paper (peer-reviewed)abstract
- In this article, we use a recurrent neural network including four-cell core architecture to model the walking gait and implement it with the simulated and physical NAO robot. Meanwhile, inspired by the biological CPG models, we propose a simplified CPG model which comprises motorneurons, interneurons, sensor neurons and the simplified spinal cord. Within this model, the CPGs do not directly output trajectories to the servo motors. Instead, they only work to maintain the phase relation among ipsilateral and contralateral limbs. The final output is dependent on the integration of CPG signals, outputs of interneurons, motor neurons and sensor neurons (sensory feedback).
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