Catherine Achard

Why Synchrony Matters during Mother- Child Interactions: A Systematic Review

Background Assessment of mother-child interactions is a core issue of early child development and psychopathology. This paper focuses on the concept of “synchrony” and examines (1) how synchrony in mother-child interaction is defined and operationalized; (2) the contribution that the concept of synchrony has brought to understanding the nature of mother-child interactions. Method Between 1977 and 2013, we searched several databases using the following key-words: « synchrony » « interaction » and « mother-child ». We focused on studies examining parent-child interactions among children aged 2 months to 5 years. From the 63 relevant studies, we extracted study description variables (authors, year, design, number of subjects, age); assessment conditions and modalities; and main findings. Results The most common terms referring to synchrony were mutuality, reciprocity, rhythmicity, harmonious interaction, turn-taking and shared affect; all terms were used to characterize the mother-child dyad. As a consequence, we propose defining synchrony as a dynamic and reciprocal adaptation of the temporal structure of behaviors and shared affect between interactive partners. Three main types of assessment methods for studying synchrony emerged: (1) global interaction scales with dyadic items; (2) specific synchrony scales; and (3) micro-coded time-series analyses. It appears that synchrony should be regarded as a social signal per se as it has been shown to be valid in both normal and pathological populations. Better mother-child synchrony is associated with familiarity (vs. unknown partner), a healthy mother (vs. pathological mother), typical development (vs. psychopathological development), and a more positive child outcomes. Discussion Synchrony is a key feature of mother-infant interactions. Adopting an objective approach in studying synchrony is not a simple task given available assessment tools and due to its temporality and multimodal expression. We propose an integrative approach combining clinical observation and engineering techniques to improve the quality of synchrony analysis.

Recognition of human behavior by space-time silhouette characterization

In this study, a method for human action recognition is proposed. Only one camera is used, without calibration. Viewpoint invariance is obtained by several acquisitions of the same action. The originality of the method consists in characterizing each sequence globally, to enhance the robustness. After detection of moving areas throughout each image, a binary volume is obtained, composed by all the silhouettes of the moving person. This space-time volume is characterized by a vector of its 3D geometric moments. These moments are normalized to be invariant to the position, scale and duration of actions. Action recognition is then carried out using a nearest neighbor classifier based on Mahanalobis distance. Results are presented on a base of 1614 sequences performed by seven persons and categorized in eight actions.

Video Sequences Association for People Re-identification across Multiple Non-overlapping Cameras

This paper presents a solution of the appearance-based people re-identification problem in a surveillance system including multiple cameras with different fields of vision. We first utilize different color-based features, combined with several illuminant invariant normalizations in order to characterize the silhouettes in static frames. A graph-based approach which is capable of learning the global structure of the manifold and preserving the properties of the original data in a lower dimensional representation is then introduced to reduce the effective working space and to realize the comparison of the video sequences. The global system was tested on a real data set collected by two cameras installed on board a train. The experimental results show that the combination of color-based features, invariant normalization procedures and the graph-based approach leads to very satisfactory results.

A novel approach for recognition of human actions with semi-global features

In this study a new approach is presented for the recognition of human actions of everyday life with a fixed camera. The originality of the presented method consists in characterizing sequences by a temporal succession of semi-global features, which are extracted from “space-time micro-volumes”. The advantage of this approach lies in the use of robust features (estimated on several frames) associated with the ability to manage actions with variable durations and easily segment the sequences with algorithms that are specific to time-varying data. Each action is actually characterized by a temporal sequence that constitutes the input of a Hidden Markov Model system for the recognition. Results presented of 1,614 sequences performed by several persons validate the proposed approach.

A sub-pixel and multispectral corner detector

Proposes in a corner detector algorithm, which leads to results both on mono-spectral and multispectral images. To validate the method, we compare its mono-spectral version to the Harris detector, which is the most frequently used in literature. This study shows that the proposed method gives generally more efficient results. However, bad localisations appear for very blurred images (as for most corner detectors). Therefore, we have implemented a sub-pixel detector able to find the exact corner position.

Automatic imitation assessment in interaction

Detecting social events such as imitation is identified as key step for the development of socially aware robots. In this paper, we present an unsupervised approach to measure immediate synchronous and asynchronous imitations between two partners. The proposed model is based on two steps: detection of interest points in images and evaluation of similarity between actions. Firstly, spatio-temporal points are detected for an accurate selection of the important information contained in videos. Then bag-of-words models are constructed, describing the visual content of videos. Finally similarity between bag-of-words models is measured with dynamic-time-warping, giving an accurate measure of imitation between partners. Experimental results obtained show that the model is able to discriminate between imitation and non-imitation phases of interactions.

People re-identification by classification of silhouettes based on sparse representation

The research presented in this paper consists in developing an automatic system for people re-identification across multiple cameras with non-overlapping fields of view. We first propose a robust algorithm for silhouette extraction which is based on an adaptive spatio-colorimetric background and foreground model coupled with a dynamic decision framework. Such a method is able to deal with the difficult conditions of outdoor environments where lighting is not stable and distracting motions are very numerous. A robust classification procedure, which exploits the discriminative nature of sparse representation, is then presented to perform people re-identification task. The global system is tested on two real data sets recorded in very difficult environments. The experimental results show that the proposed system leads to very satisfactory results compared to other approaches of the literature.

Interaction and behaviour imaging: a novel method to measure mother–infant interaction using video 3D reconstruction

Studying early interaction is essential for understanding development and psychopathology. Automatic computational methods offer the possibility to analyse social signals and behaviours of several partners simultaneously and dynamically. Here, 20 dyads of mothers and their 13–36-month-old infants were videotaped during mother–infant interaction including 10 extremely high-risk and 10 low-risk dyads using two-dimensional (2D) and three-dimensional (3D) sensors. From 2D+3D data and 3D space reconstruction, we extracted individual parameters (quantity of movement and motion activity ratio for each partner) and dyadic parameters related to the dynamics of partners heads distance (contribution to heads distance), to the focus of mutual engagement (percentage of time spent face to face or oriented to the task) and to the dynamics of motion activity (synchrony ratio, overlap ratio, pause ratio). Features are compared with blind global rating of the interaction using the coding interactive behavior (CIB). We found that individual and dyadic parameters of 2D+3D motion features perfectly correlates with rated CIB maternal and dyadic composite scores. Support Vector Machine classification using all 2D–3D motion features classified 100% of the dyads in their group meaning that motion behaviours are sufficient to distinguish high-risk from low-risk dyads. The proposed method may present a promising, low-cost methodology that can uniquely use artificial technology to detect meaningful features of human interactions and may have several implications for studying dyadic behaviours in psychiatry. Combining both global rating scales and computerized methods may enable a continuum of time scale from a summary of entire interactions to second-by-second dynamics.

Social signal processing for studying parent-infant interaction

Studying early interactions is a core issue of infant development and psychopathology. Automatic social signal processing theoretically offers the possibility to extract and analyze communication by taking an integrative perspective, considering the multimodal nature and dynamics of behaviors (including synchrony). This paper proposes an explorative method to acquire and extract relevant social signals from a naturalistic early parent–infant interaction. An experimental setup is proposed based on both clinical and technical requirements. We extracted various cues from body postures and speech productions of partners using the IMI2S (Interaction, Multimodal Integration, and Social Signal) Framework. Preliminary clinical and computational results are reported for two dyads (one pathological in a situation of severe emotional neglect and one normal control) as an illustration of our cross-disciplinary protocol. The results from both clinical and computational analyzes highlight similar differences: the pathological dyad shows dyssynchronic interaction led by the infant whereas the control dyad shows synchronic interaction and a smooth interactive dialog. The results suggest that the current method might be promising for future studies.

Action recognition with global features

In this study, a new method allowing recognizing and segmenting everyday life actions is proposed. Only one camera is utilized without calibration. Viewpoint invariance is obtained by several acquisitions of the same action. To enhance robustness, each sequence is characterized globally: a detection of moving areas is first computed on each image. All these binary points form a volume in the three-dimensional (3D) space (x,y,t). This volume is characterized by its geometric 3D moments. Action recognition is then carried out by computing the Mahalanobis distance between the vector of features of the action to be recognized and those of the reference database. Results, which validate the suggested approach, are presented on a base of 1662 sequences performed by several persons and categorized in eight actions. An extension of the method for the segmentation of sequences with several actions is also proposed.