Ecological restoration and rewilding: two approaches with complementary goals?

As we enter the UN Decade on Ecosystem Restoration (2021-2030) and address the urgent need to protect and restore ecosystems and their ecological functions at large scales, rewilding has been brought into the limelight. Interest in this discipline is thus increasing, with a large number of conceptual scientific papers published in recent years. Increasing enthusiasm has led to discussions and debates in the scientific community about the differences between ecological restoration and rewilding. The main goal of this review is to compare and clarify the position of each field. Our results show that despite some differences (e.g. top-down versus bottom-up and functional versus taxonomic approaches) and notably with distinct goals - recovery of a defined historically determined target ecosystem versus recovery of natural processes with often no target endpoint - ecological restoration and rewilding have a common scope: the recovery of ecosystems following anthropogenic degradation. The goals of ecological restoration and rewilding have expanded with the progress of each field. However, it is unclear whether there is a paradigm shift with ecological restoration moving towards rewilding or vice versa. We underline the complementarity in time and in space of ecological restoration and rewilding. To conclude, we argue that reconciliation of these two fields of nature conservation to ensure complementarity could create a synergy to achieve their common scope.

A Saliency based Feature Fusion Model for EEG Emotion Estimation.

Among the different modalities to assess emotion, electroencephalogram (EEG), representing the electrical brain activity, achieved motivating results over the last decade. Emotion estimation from EEG could help in the diagnosis or rehabilitation of certain diseases. In this paper, we propose a dual model considering two different representations of EEG feature maps: 1) a sequential based representation of EEG band power, 2) an image-based representation of the feature vectors. We also propose an innovative method to combine the information based on a saliency analysis of the image-based model to promote joint learning of both model parts. The model has been evaluated on four publicly available datasets: SEED-IV, SEED, DEAP and MPED. The achieved results outperform results from state-of-the-art approaches for three of the proposed datasets with a lower standard deviation that reflects higher stability. For sake of reproducibility, the codes and models proposed in this paper are available at https://github.com/VDelv/Emotion-EEG.

Pooling biodiversity offsets to improve habitat connectivity and species conservation.

Land developers can apply biodiversity offsetting in different ways, from a project-by-project approach to a pooled and proactive approach, this latter appearing to provide greater advantages both in terms of implementation and of the No Net Loss objective. Incorporating landscape connectivity into the mitigation hierarchy is commonly recommended, but the benefits of pooling and anticipating offsets have never really been demonstrated from modeling approaches. Here, we compare connectivity gains from two different offsetting scenarios, when interconnections at offset sites are taken and not taken into account. Assuming that gains can be increased by optimizing the location of offsets, we identified sites where biodiversity offsetting generates the greatest ecological gains in habitat connectivity. The method was applied to a study case in the suburbs of Lyon (Southern France) using several representative species and the landscape functional connectivity model Graphab. Pooling biodiversity offsets led to additional gains in overall habitat connectivity of +103% on average, which we show can be further improved (+8%) by using a patch addition process available in Graphab to plan spatially and ecologically coherent offsetting areas. Pooling and anticipating biodiversity offsets in this way can help preserve the biodiversity and the functionality of natural environments at the territorial scale.

Maximizing habitat connectivity in the mitigation hierarchy. A case study on three terrestrial mammals in an urban environment.

Environmental policies and the objective of no net loss highlight the importance of preserving ecological networks to limit the fragmentation of natural habitats and biodiversity loss, especially due to urbanization. In the environmental impact assessment context, habitat connectivity and the spatio-temporal dynamics of biodiversity are crucial to obtaining reliable predictions that can support decision-making. We propose a methodological framework 1) to quantify the overall impact of a development project on the functioning of an ecological network, and 2) to select the best locations for implanting new habitat patches intended to enhance landscape connectivity. The amount of reachable habitat concept was applied to three representative terrestrial mammal species: the red squirrel, the Eurasian badger and the European hedgehog. All three species are recognized as vulnerable to human pressures and potentially affected by the construction of a new stadium in our study site, Lyon (Southern France). The method combines the species distribution model Maxent with the landscape functional connectivity model Graphab. The results showed that using any one of the avoidance and reduction measures on its own was unsuccessful in achieving the objective of no net loss when habitat connectivity is considered. However, the combination of new habitat patches and corridors offered a higher gain than distinct measures. This is especially important in the short term, when new hedgerow plantations have not yet developed enough to be used by the target species. Our findings indicate, first, the need to take the temporal scale into account in environmental impact assessment. We also show that applying the optimal scenario, constructed using a cumulative patch addition followed by a similar process testing a set of potential land-use changes, maximizes habitat connectivity. Our methodology provides a useful tool to increase target species' habitat connectivity within the mitigation hierarchy and to enhance development project design for increased environmental efficiency.

Environmental impact assessment of development projects improved by merging species distribution and habitat connectivity modelling.

Environmental impact assessment (EIA) is performed to limit potential impacts of development projects on species and ecosystem functions. However, the methods related to EIA actually pay little attention to the landscape-scale effects of development projects on biodiversity. In this study we proposed a methodological framework to more properly address the landscape-scale impacts of a new stadium project in Lyon (France) on two representative mammal species exemplary for the endemic fauna, the red squirrel and the Eurasian badger. Our approach combined species distribution model using Maxent and landscape functional connectivity model using Graphab at two spatial scales to assess habitat connectivity before and after development project implementation. The development project had a negative impact on landscape connectivity: overall habitat connectivity (PC index) decreased by -6.8% and -1.8% and the number of graph components increased by +60.0% and +17.6% for the red squirrel and the European badger respectively, because some links that formerly connected habitat patches were cut by the development project. Changes affecting landscape structure and composition emphasized the need to implement appropriate avoidance and reduction measures. Our methodology provides a useful tool both for EIA studies at each step of the way to support decision-making in landscape conservation planning. The method could be also developed in the design phase to compare the effectiveness of different avoidance or mitigation measures and resize them if necessary to maximize habitat connectivity.

UMONS-TAICHI: A multimodal motion capture dataset of expertise in Taijiquan gestures.

In this article, we present a large 3D motion capture dataset of Taijiquan martial art gestures (n = 2200 samples) that includes 13 classes (relative to Taijiquan techniques) executed by 12 participants of various skill levels. Participants levels were ranked by three experts on a scale of [0-10]. The dataset was captured using two motion capture systems simultaneously: 1) Qualisys, a sophisticated optical motion capture system of 11 cameras that tracks 68 retroreflective markers at 179 Hz, and 2) Microsoft Kinect V2, a low-cost markerless time-of-flight depth sensor that tracks 25 locations of a person׳s skeleton at 30 Hz. Data from both systems were synchronized manually. Qualisys data were manually corrected, and then processed to complete any missing data. Data were also manually annotated for segmentation. Both segmented and unsegmented data are provided in this dataset. This article details the recording protocol as well as the processing and annotation procedures. The data were initially recorded for gesture recognition and skill evaluation, but they are also suited for research on synthesis, segmentation, multi-sensor data comparison and fusion, sports science or more general research on human science or motion capture. A preliminary analysis has been conducted by Tits et al. (2017) [1] on a part of the dataset to extract morphology-independent motion features for skill evaluation. Results of this analysis are presented in their communication: "Morphology Independent Feature Engineering in Motion Capture Database for Gesture Evaluation" (10.1145/3077981.3078037) [1]. Data are available for research purpose (license CC BY-NC-SA 4.0), at https://github.com/numediart/UMONS-TAICHI.

Robust and automatic motion-capture data recovery using soft skeleton constraints and model averaging.

Motion capture allows accurate recording of human motion, with applications in many fields, including entertainment, medicine, sports science and human computer interaction. A common difficulty with this technology is the occurrence of missing data, due to occlusions, or recording conditions. Various models have been proposed to estimate missing data. Some are based on interpolation, low-rank properties or inter-correlations. Others involve dataset matching or skeleton constraints. While the latter have the advantage of promoting a realistic motion estimation, they require prior knowledge of skeleton constraints, or the availability of a prerecorded dataset. In this article, we propose a probabilistic averaging method of several recovery models (referred to as Probabilistic Model Averaging (PMA) in this paper), based on the likelihoods of the distances between body points. This method has the advantage of being automatic, while allowing an efficient gap data recovery. To support and validate the proposed method, we use a set of four individual recovery models, based on linear/nonlinear regression in local coordinate systems. Finally, we propose two heuristic algorithms to enforce skeleton constraints in the reconstructed motion, which can be used on any individual recovery model. For validation purposes, random gaps were introduced into motion-capture sequences, and the effects of factors such as the number of simultaneous gaps, gap length and sequence duration were analyzed. Results show that the proposed probabilistic averaging method yields better recovery than (i) each of the four individual models and (ii) two recent state-of-the-art models, regardless of gap length, sequence duration and number of simultaneous gaps. Moreover, both of our heuristic skeleton-constraint algorithms significantly improve the recovery for 7 out of 8 tested motion-capture sequences (p < 0.05), for 10 simultaneous gaps of 5 seconds. The code is available for free download at: https://github.com/numediart/MocapRecovery.

Adaptive differentiation among populations of the Mediterranean dry grassland species Brachypodium retusum: The role of soil conditions, grazing, and humidity.

PREMISE OF THE STUDY: Genetic differentiation in plant species may result from adaptation to environmental conditions, but also from stochastic processes. The drivers selecting for local adaptation and the contribution of adaptation to genetic differentiation are often unknown. Restoration and succession studies have revealed different colonization patterns for Brachypodium retusum, a common Mediterranean grass. In order to understand these patterns, we tested population differentiation and adaptation to different environmental factors. METHODS: Structured sampling of 12 populations from six sites and two soil types within site was used to analyze the spatial and environmental structure of population differentiation. Sampling sites differ in grazing intensity and climate. We tested germination and growth in a common garden. In subsets, we analyzed the differential response to stone cover, grazing and soil moisture. KEY RESULTS: We found significant differences among populations. The site explained population differentiation better than soil, suggesting a dominant influence of climate and/or genetic drift. Stone cover had a positive influence on seedling establishment, and populations showed a differential response. However, this response was not related to environmental differences between collection sites. Regrowth after clipping was higher in populations from the more intensively grazed Red Mediterranean soils suggesting an adaptation to grazing. Final germination was generally high even under drought, but germination response to differences in soil moisture was similar across populations. CONCLUSIONS: Adaptive population differentiation in germination and early growth may have contributed to different colonization patterns. Thus, the provenance of B. retusum needs to be carefully considered in ecological restoration.

Using Different Grazing Practices for Increasing Plant Biodiversity in the Dykes and Embankments Along the Rhône River (Southern France).

Extensive grazing by domestic herbivores is a widespread management practice used since the 80s in many European agro-ecosystems such as semi-natural grasslands to maintain open habitats and to enhance biodiversity. Such grazing systems have principally been tested in cultural ecosystems of high nature value threatened by grazing abandonment. However, there have been few case studies of grazing management in very anthropized ecosystems, such as the new ecosystems created by urban or industrial conversions. In Southern France, the Rhône channeling for navigation and electricity production generated in the 1950s the construction of thousands of hectares of dams and dykes which were colonized naturally by diverse plant communities. Yet shrub encroachment and the consequent recourse to mechanical cutting to facilitate control and maintenance, raise the question of how best to maintain and manage these new habitats. Consequently, since 1999, different low-intensity grazing management systems using rustic breeds of cattle, horses and goats have been tested on a protected reserve of 1454 ha located in the lower part of the Rhône river. Extensive grazing, more than cutting or no management, positively modified vegetation heterogeneity (beta-diversity), the target open grassland species, but not plant species richness (alpha-diversity). However, the current monitoring shows that these benefits of grazing will be confirmed only if low-intensity grazing systems are sustained and if new adaptations can be also made, such as the use of mixed stocking and the establishment of multiyear contracts with breeders.

Selection of native plants with phytoremediation potential for highly contaminated Mediterranean soil restoration: Tools for a non-destructive and integrative approach.

The aim of this study was to develop an effective and non-destructive method for the selection of native Mediterranean plants with phytoremediation potential based on their spontaneous recovery capacities. The study site consisted in a mixed contaminated soils (As, Cu, Pb, Sb, Zn) in the vicinity of a former lead smelting factory abandoned since 1925 in the Calanques National Park (Marseille, southeastern France). We developed an integrated characterization approach that takes into account topsoil metal(loid)s (MM) contamination, plant community composition and structure and mesologic parameters without using destructive methods. From a statistical selection of significant environmental descriptors, plant communities were described and interpreted as the result of spontaneous recovery under multiple stresses and local conditions (both natural and anthropogenic). We collected phytoecological and MM topsoil data using field monitoring and geographic information system (GIS) on a pollution hotspot where natural plant communities occur. The results of the multivariate analysis performed between species and descriptors indicated that a century of MM pollution pressure produced a significant correlation with plant community dynamics in terms of composition, diversity and structure, leading to the co-occurrence of different plant succession stages. Thus, these successions seemed linked to the variability of anthropogenic disturbance regimes within the study site. We recorded high topsoil contamination heterogeneity at the scale both of the plot and of the whole study area that suggested a heterogeneous MM distribution pattern dependent on the source of contaminants and site environmental variability. We identified 4 spontaneous plant communities co-occurring through a MM contamination gradient that could be used later from degraded to reference communities to define ecological restoration target combined to phytoremediation applications with respect to local conditions. Our results suggested that some of the native plant species such as Coronilla juncea and Globularia alypum might be tolerant to high mixed MM soil concentrations and they could thus be used for phytostabilization purposes in polluted Mediterranean areas in regard to their life-traits. Our non-destructive methodology led both to the selection of tolerant native plant species and communities and identification of highly polluted priority intervention areas through the study site where phytostabilization should be implemented. Furthermore, by analyzing succession dynamics linked to contamination patterns throughout the area and spontaneous recovery of native tolerant vegetation, our methodology opens up broad perspectives and research fields for ecological restoration for Mediterranean protected and contaminated areas based on ecosystem trajectories and new approaches for the integrative management of polluted soils.

A Novel Method for Screening Children with Isolated Bicuspid Aortic Valve.

This paper presents a novel processing method for heart sound signal: the statistical time growing neural network (STGNN). The STGNN performs a robust classification by merging supervised and unsupervised statistical methods to overcome non-stationary behavior of the signal. By combining available preprocessing and segmentation techniques and the STGNN classifier, we build an automatic tool for screening children with isolated BAV, the congenital heart malformation which can lead to serious cardiovascular lesions. Children with BAV (22 individuals) and healthy condition (28 individuals) are subjected to the study. The performance of the STGNN is compared to that of a time growing neural network (CTGNN) and a conventional support vector (CSVM) machine, using balanced repeated random sub sampling. The average of the accuracy/sensitivity is estimated to be 87.4/86.5 for the STGNN, 81.8/83.4 for the CTGNN, and 72.9/66.8 for the CSVM. Results show that the STGNN offers better performance and provides more immunity to the background noise as compared to the CTGNN and CSVM. The method is implementable in a computer system to be employed in primary healthcare centers to improve the screening accuracy.

Using Shrub Clearing, Draining, and Herbivory to Control Bramble Invasion in Mediterranean Dry Grasslands.

For centuries, the dry grassland of the plain of La Crau (south-eastern France) has been subjected to numerous disturbances resulting in the destruction and the fragmentation of this emblematic rangeland ecosystem of the Mediterranean. Today, this ecosystem is facing a new threat from a proliferating native species, the bramble (Rubus ulmifolius Schott), which preferentially colonizes areas that were formerly cultivated and/or exposed to water infiltration. To identify a strategy for effective control of this colonization, in situ experiments testing disturbance regimes (shrub clearing and/or mixed grazing by sheep and goats) combined with the control of access to water resources (with or without drainage trenches) were undertaken between 2010 and 2013. Only clearing and grazing combined over 3 years led to significant changes in vegetation height and bramble cover as well as modifications in the floristic composition, diversity, similarity, and richness of the plant community. Neither a clearing operation carried out in 2010 alone, nor grazing alone, reduced bramble cover, and neither treatment increased the species richness of the plant community. Similarly, digging drainage trenches had no significant impact either on the plant community or on bramble cover. Our study suggests that only annual mechanical clearing coupled with sheep and goats grazing can significantly reduce bramble cover. This combined restoration treatment needs to be applied for at least 3 consecutive years to induce significant changes and enable this ecosystem to return to the dry grassland succession.

Detection of systolic ejection click using time growing neural network.

In this paper, we present a novel neural network for classification of short-duration heart sounds: the time growing neural network (TGNN). The input to the network is the spectral power in adjacent frequency bands as computed in time windows of growing length. Children with heart systolic ejection click (SEC) and normal children are the two groups subjected to analysis. The performance of the TGNN is compared to that of a time delay neural network (TDNN) and a multi-layer perceptron (MLP), using training and test datasets of similar sizes with a total of 614 normal and abnormal cardiac cycles. From the test dataset, the classification rate/sensitivity is found to be 97.0%/98.1% for the TGNN, 85.1%/76.4% for the TDNN, and 92.7%/85.7% for the MLP. The results show that the TGNN performs better than do TDNN and MLP when frequency band power is used as classifier input. The performance of TGNN is also found to exhibit better immunity to noise.

About the cortical origin of the low-delta and high-gamma rhythms observed in EEG signals during treadmill walking.

This paper presents a spectral and time-frequency analysis of EEG signals recorded on seven healthy subjects walking on a treadmill at three different speeds. An accelerometer was placed on the head of the subjects in order to record the shocks undergone by the EEG electrodes during walking. Our results indicate that up to 15 harmonics of the fundamental stepping frequency may pollute EEG signals, depending on the walking speed and also on the electrode location. This finding may call into question some conclusions drawn in previous EEG studies where low-delta band (especially around 1 Hz, the fundamental stepping frequency) had been announced as being the seat of angular and linear kinematics control of the lower limbs during walk. Additionally, our analysis reveals that EEG and accelerometer signals exhibit similar time-frequency properties, especially in frequency bands extending up to 150 Hz, suggesting that previous conclusions claiming the activation of high-gamma rhythms during walking may have been drawn on the basis of insufficiently cleaned EEG signals. Our results are put in perspective with recent EEG studies related to locomotion and extensively discussed in particular by focusing on the low-delta and high-gamma bands.

Performance of the Emotiv Epoc headset for P300-based applications.

BACKGROUND: For two decades, EEG-based Brain-Computer Interface (BCI) systems have been widely studied in research labs. Now, researchers want to consider out-of-the-lab applications and make this technology available to everybody. However, medical-grade EEG recording devices are still much too expensive for end-users, especially disabled people. Therefore, several low-cost alternatives have appeared on the market. The Emotiv Epoc headset is one of them. Although some previous work showed this device could suit the customer's needs in terms of performance, no quantitative classification-based assessments compared to a medical system are available. METHODS: This paper aims at statistically comparing a medical-grade system, the ANT device, and the Emotiv Epoc headset by determining their respective performances in a P300 BCI using the same electrodes. On top of that, a review of previous Emotiv studies and a discussion on practical considerations regarding both systems are proposed. Nine healthy subjects participated in this experiment during which the ANT and the Emotiv systems are used in two different conditions: sitting on a chair and walking on a treadmill at constant speed. RESULTS: The Emotiv headset performs significantly worse than the medical device; observed effect sizes vary from medium to large. The Emotiv headset has higher relative operational and maintenance costs than its medical-grade competitor. CONCLUSIONS: Although this low-cost headset is able to record EEG data in a satisfying manner, it should only be chosen for non critical applications such as games, communication systems, etc. For rehabilitation or prosthesis control, this lack of reliability may lead to serious consequences. For research purposes, the medical system should be chosen except if a lot of trials are available or when the Signal-to-Noise Ratio is high. This also suggests that the design of a specific low-cost EEG recording system for critical applications and research is still required.

Objective study of sensor relevance for automatic cough detection.

The development of a system for the automatic, objective, and reliable detection of cough events is a need underlined by the medical literature for years. The benefit of such a tool is clear as it would allow the assessment of pathology severity in chronic cough diseases. Even though some approaches have recently reported solutions achieving this task with a relative success, there is still no standardization about the method to adopt or the sensors to use. The goal of this paper is to study objectively the performance of several sensors for cough detection: ECG, thermistor, chest belt, accelerometer, contact, and audio microphones. Experiments are carried out on a database of 32 healthy subjects producing, in a confined room and in three situations, voluntary cough at various volumes as well as other event categories which can possibly lead to some detection errors: background noise, forced expiration, throat clearing, speech, and laugh. The relevance of each sensor is evaluated at three stages: mutual information conveyed by the features, ability to discriminate at the frame level cough from these latter other sources of ambiguity, and ability to detect cough events. In this latter experiment, with both an averaged sensitivity and specificity of about 94.5%, the proposed approach is shown to clearly outperform the commercial Karmelsonix system which achieved a specificity of 95.3% and a sensitivity of 64.9%.

Towards effective non-invasive brain-computer interfaces dedicated to gait rehabilitation systems.

In the last few years, significant progress has been made in the field of walk rehabilitation. Motor cortex signals in bipedal monkeys have been interpreted to predict walk kinematics. Epidural electrical stimulation in rats and in one young paraplegic has been realized to partially restore motor control after spinal cord injury. However, these experimental trials are far from being applicable to all patients suffering from motor impairments. Therefore, it is thought that more simple rehabilitation systems are desirable in the meanwhile. The goal of this review is to describe and summarize the progress made in the development of non-invasive brain-computer interfaces dedicated to motor rehabilitation systems. In the first part, the main principles of human locomotion control are presented. The paper then focuses on the mechanisms of supra-spinal centers active during gait, including results from electroencephalography, functional brain imaging technologies [near-infrared spectroscopy (NIRS), functional magnetic resonance imaging (fMRI), positron-emission tomography (PET), single-photon emission-computed tomography (SPECT)] and invasive studies. The first brain-computer interface (BCI) applications to gait rehabilitation are then presented, with a discussion about the different strategies developed in the field. The challenges to raise for future systems are identified and discussed. Finally, we present some proposals to address these challenges, in order to contribute to the improvement of BCI for gait rehabilitation.

A subjective assessment of a P300 BCI system for lower-limb rehabilitation purposes.

Recent research has shown that a P300 system can be used while walking without requiring any specific gait-related artifact removal techniques. Also, standard EEG-based Brain-Computer Interfaces (BCI) have not been really assessed for lower limb rehabilitation/prosthesis. Therefore, this paper gives a first baseline estimation (for future BCI comparisons) of the subjective and objective performances of a four-state P300 BCI plus a non-control state for lower-limb rehabilitation purposes. To assess usability and workload, the System Usability Scale and the NASA Task Load Index questionnaires were administered to five healthy subjects after performing a real-time treadmill speed control. Results show that the P300 BCI approach could suit fitness and rehabilitation applications, whereas prosthesis control, which suffers from a low reactivity, appears too sensitive for risky and crowded areas.

Germination traits explain soil seed persistence across species: the case of Mediterranean annual plants in cereal fields.

BACKGROUND AND AIMS: Seed persistence in the soil under field conditions is an important issue for the maintenance of local plant populations and the restoration of plant communities, increasingly so in the light of rapidly changing land use and climate change. Whereas processes important for dispersal in space are well known, knowledge of processes governing dispersal in time is still limited. Data for morphological seed traits such as size have given contradictory results for prediction of soil seed persistence or cover only a few species. There have been few experimental studies on the role of germination traits in determining soil seed persistence, while none has studied their predictive value consistently across species. Delayed germination, as well as light requirements for germination, have been suggested to contribute to the formation of persistent seed banks. Moreover, diurnally fluctuating temperatures can influence the timing of germination and are therefore linked to seed bank persistence. METHODS: The role of germination speed measured by T(50) (days to germination of 50 % of all germinated seeds), light requirement and reaction to diurnally fluctuating temperatures in determining seed persistence in the soil was evaluated using an experimental comparative data set of 25 annual cereal weed species. KEY RESULTS: It is shown that light requirements and slow germination are important features to maintain seeds ungerminated just after entering the soil, and hence influence survival of seeds in the soil. However, the detection of low diurnally fluctuating temperatures enhances soil seed bank persistence by limiting germination. Our data further suggest that the effect of diurnally fluctuating temperatures, as measured on seeds after dispersal and dry storage, is increasingly important to prevent fatal germination after longer burial periods. CONCLUSIONS: These results underline the functional role of delayed germination and light for survival of seeds in the soil and hence their importance for shaping the first part of the seed decay curve. Our analyses highlight the detection of diurnally fluctuating temperatures as a third mechanism to achieve higher soil seed persistence after burial which interacts strongly with season. We therefore advocate focusing future research on mechanisms that favour soil seed persistence after longer burial times and moving from studies of morphological features to exploration of germination traits such as reaction to diurnally fluctuating temperatures.

An active foot lifter orthosis based on a PCPG algorithm.

Central pattern generators (CPGs) are known to play an important role in the generation of rhythmic movements in gait, both in animals and humans. The comprehension of their underlying mechanism has led to the development of an important family of algorithms at the basis of autonomous walking robots. Recently, it has been shown that human gait could be modeled using a subclass of those algorithms, namely a Programmable Central Pattern Generator (PCPG). In this paper, we present a foot lifter orthosis driven by this algorithm. After a learning phase, the PCPG is able to generate adequate rhythmic gait patterns both for constant speeds and acceleration phases. Its output is used to drive the orthosis actuator during the swing phase, in order to help patients suffering from foot drop (the orthosis just follows the movement during the stance phase). The most interesting property of this algorithm is the possibility to generate a smooth output signal even during speed transitions. In practice, given that human gait is not perfectly periodic, the phase of this signal needs to be reset with actual movement. Therefore, two phase-resetting procedures were studied: one standard hard phase-resetting leading to discontinuities and one original soft phase-resetting allowing to recover the correct phase in a smooth way. The simulation results and complete design of the orthosis hardware and software are presented.