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Falls are an urgent challenge in aging societies, and balance dysfunction is a major risk factor. Current robotic technology that assists human locomotion, however, aims at versatile functionality, particularly in the assistance of weak muscles. Such versatile design leads to heavy, bulky devices that are impractical in daily life for most elderly subjects. In this paper, we investigate the use of...
Some home monitoring research has tried to deal with a broad range of human behaviors such Activity of Daily Living (ADL) or Instrumented ADL (IADL) in an entire home, but most research results does not yet seem satisfactory. So we focused on modeling human behaviors in a bedroom, not an entire home. To do this, we define a behavior state and behaviors. We constructed the experimental system by installing...
An innovative way of object shape representation using Density Histogram of Feature Points (DHFP) is introduced and used in this paper. We have named this method Enhanced Density Histogram of Feature Points (EDHFP). We use silhouette images where the image region ξ consists of only those pixels that correspond to points on the object and have a value one (1) indicating “on” pixels. We count the number...
Posture measurement systems using infrared or vision cameras require a special setting and thus it is difficult to use in daily lives. In this paper, a mobile three-dimensional posture measurement system is developed based on inertial sensors and smart shoes. Since the signals are measured and processed by a mobile computer, the proposed system enables the analysis and diagnosis of postures during...
It is necessary for a blind person to know the locations of the pedestrians or moving objects around him/her on a walk road for his/her safe walking. This paper proposes a system for detecting pedestrians or moving objects in front of a person on a walk road by a camera mounted on his/her body. The system judges if a moving object in front of the person is going to hit him/her in the near future by...
An adaptive synergy controller is presented which autonomously modulates the finger synergies of a dexterous robotic hand according to the relative orientation of a grasped object. The adaptive synergy controller is derived from approximating the human motion of unscrewing a bottle cap with sinusoids to replicate the task with a robotic hand. In preliminary experiments, the finger joint motions of...
We present an unsupervised method of learning action symbols from video data, which self-tunes the number of symbols to effectively build hierarchical activity grammars. A video stream is given as a sequence of unlabeled segments. Similar segments are incrementally grouped to form a hierarchical tree structure. The tree is cut into clusters where each cluster is used to train an action symbol. Our...
This work introduces a new representation for Motion Capture data (MoCap) that is invariant under rigid transformation and robust for classification and annotation of MoCap data. This representation relies on distance matrices that fully characterize the class of identical postures up to the body position or orientation. This high dimensional feature descriptor is tailored using PCA and incorporated...
In the literature of human action recognition, despite promising results have been obtained by the traditional bag-of-words model, the relationship among spatiotemporal points has rarely been considered. Furthermore, serious quantization error also exists in this kind of strategy. In this paper, we propose a novel coding strategy named contextual Fisher kernels to overcome these limitations. We add...
We present a method for human action recognition based on the combination of Histograms of Gradients into orientation tensors. It uses only information from HOG3D: no features or points of interest are extracted. The resulting raw histograms obtained per frame are combined into an orientation tensor, making it a simple, fast to compute and effective global descriptor. The addition of new videos and/or...
Many human actions are correlated, because of compound and/or sequential actions, and similarity. Indeed, human actions are highly correlated in human annotations of 48 actions in the 4,774 videos from visint.org. We exploit such correlations to improve the detection of these 48 human actions, ranging from simple actions such as walk to complex actions such as exchange. We apply a basic pipeline of...
Pedestrian detection problem has been a touchstone of various image feature descriptors. In this paper, we evaluate four kinds of representative local descriptors (HOG, Haar-like, SURF and LBP) for pedestrian representation. Our goal is to find out the best combination of feature descriptors by analyzing and evaluating the complementarities of them. With the cross validation method, we first find...
In this paper, we propose an approach for human activity categorizing based on the use of optical flow direction and magnitude features. The main contribution of this paper is the feature representation that mirrors the geometry of the human body and relationships between its moving regions when performing activities. The features are quantified using a quantization algorithm. We analyze the performance...
The aim of this paper is to track objects during their use by humans. The task is difficult because these objects are small, fast-moving and often occluded by the user. We present a novel solution based on cascade action recognition, a learned mapping between body-and object-poses, and a hierarchical extension of importance sampling. During tracking, body pose estimates from a Kinect sensor are classified...
Human action recognition is an active area with applications in several domains such as visual surveillance, video retrieval and human-computer interaction. Current approaches assign action labels to video streams considering the whole video as a single image sequence. Such approaches, albeit very refined, may fail on some samples due to large variability between frames, suggesting that features extracted...
This paper tackles a challenging problem of inertial sensor-based recognition for similar walking action classes. We solve two remaining problems of existing methods in the case of walking actions: action signal segmentation and recognition of similar action classes. First, to robustly segment the walking action under drastic changes such as speed, intensity, or style, we rely on the likelihood of...
We report on the moving hand gesture recognition technique using Adaptive Resonance Theory (ART). To detect the start and end points of a continuous moving gesture (known as “gesture spotting” problem), we propose the adaptive distributed prediction technique. Our results show that, unlike conventional non-recurrent neural networks, the proposed technique can be utilized usefully in reliable real-time...
For achieving efficient action recognition, some recent works propose to select a smaller number of frames in a video sequence instead of the entire sequence of frames. In this study, we propose to represent a frame by a combination of local and global descriptors instead of the silhouette used in our previous approach aiming at frame selection. Action recognition is then executed on the basis of...
Biometrics-based authentication is a method of personal identification that has some advantages over the password and object-based ones, mainly for the user, who doesn't need to carry or memorize anything. However, this kind of identification is also subject to problems. Besides the technology-related possibilities of fraud, such as system invasion, database corruption or algorithm injection, some...
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