In order for cooperative robots ("co-robots") to respond to human behaviors accurately and efficiently in human-robot collaboration, interpretation of human actions, awareness of new situations, and appropriate decision making are all crucial abilities for co-robots. For this purpose, the human behaviors should be interpreted by co-robots in the same manner as human peers. To address this issue, a novel interpretability indicator is introduced so that robot actions are appropriate to the current human behaviors. In addition, the complete consideration of all potential situations of a robot's environment is nearly impossible in real-world applications, making it difficult for the co-robot to act appropriately and safely in new scenarios. This is true even when the pretrained model is highly accurate in a known situation. For effective and safe teaming with humans, we introduce a new generalizability indicator that allows a co-robot to self-reflect and reason about when an observation falls outside the co-robot's learned model. Based on topic modeling and two novel indicators, we propose a new Self-reflective Risk-aware Artificial Cognitive (SRAC) model. The co-robots are able to consider action risks and identify new situations so that better decisions can be made. Experiments both using real-world datasets and on physical robots suggest that our SRAC model significantly outperforms the traditional methodology and enables better decision making in response to human activities.
Space-time volume methods use local features to represent local texture and motion variations regardless of global human appearance and activity. A large number of HAR methods are based on SIFT features @cite_26 and its extensions @cite_37 . For example, Behera proposed a random forest that unifies randomization, discriminative relationships mining and a Markov temporal structure for real-time activity recognition with SIFT features @cite_48 . However, SIFT features only encode appearance information and are not able to represent temporal information. STIP features were introduced in @cite_10 and SVMs were applied to classify human activities. Dollar used separable filters in spatial and temporal dimensions to extract features for HAR @cite_19 . Four-dimensional features were also introduced in @cite_43 to combine depth information to classify human activities.
@cite_37 @cite_26 @cite_48 @cite_19 @cite_43 @cite_10
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This paper focuses on human activity recognition (HAR) problem, in which inputs are multichannel time series signals acquired from a set of bodyworn inertial sensors and outputs are predefined human activities. In this problem, extracting effective features for identifying activities is a critical but challenging task. Most existing work relies on heuristic hand-crafted feature design and shallow feature learning architectures, which cannot find those distinguishing features to accurately classify different activities. In this paper, we propose a systematic feature learning method for HAR problem. This method adopts a deep convolutional neural networks (CNN) to automate feature learning from the raw inputs in a systematic way. Through the deep architecture, the learned features are deemed as the higher level abstract representation of low level raw time series signals. By leveraging the labelled information via supervised learning, the learned features are endowed with more discriminative power. Unified in one model, feature learning and classification are mutually enhanced. All these unique advantages of the CNN make it outperform other HAR algorithms, as verified in the experiments on the Opportunity Activity Recognition Challenge and other benchmark datasets. This paper presents a space-time extension of scale-invariant feature transform (SIFT) originally applied to the 2-dimensional (2D) volumetric images. Most of the previous extensions dealt with 3-dimensional (3D) spacial information using a combination of a 2D detector and a 3D descriptor for applications such as medical image analysis. In this work we build a spatio-temporal difference-of-Gaussian (DoG) pyramid to detect the local extrema, aiming at processing video streams. Interest points are extracted not only from the spatial plane (xy) but also from the planes along the time axis (xt and yt). The space-time extension was evaluated using the human action classification task. Experiments with the KTH and the UCF sports datasets show that the approach was able to produce results comparable to the state-of-the-arts. With the rapid development of wearable sensors and pervasive computing technologies including smartphones, acceleration-based human activity recognition is receiving increased attention for medical research applications. Motivated by the “weightlessness” feature, here we apply a bidirectional feature during the feature extraction phase of activity recognition; however, since the bidirectional feature has two components, they cannot simply be concatenated into a long vector, but can be naturally treated as a second-order tensor. Therefore, we propose a new tensor-based feature selection method termed tensor manifold discriminant projections (TMDP). TMDP simultaneously considers: 1) applying an optimization criterion that can directly process the tensor spectral analysis problem, thereby decreasing the computational cost compared to traditional tensor-based feature selection methods; 2) extracting local rank information by finding a tensor subspace that preserves the rank order information of the within-class input samples; and 3) extracting discriminant information by maximizing the sum of distances between every sample and their interclass sample mean. Experiments on the naturalistic mobile devices-based human activity 2.0 dataset are performed to demonstrate the effectiveness and robustness of TMDP. In this paper, we present a novel method to explore semantically meaningful visual information and identify the discriminative spatiotemporal relationships between them for real-time activity recognition. Our approach infers human activities using continuous egocentric (first-person-view) videos of object manipulations in an industrial setup. In order to achieve this goal, we propose a random forest that unifies randomization, discriminative relationships mining and a Markov temporal structure. Discriminative relationships mining helps us to model relations that distinguish different activities, while randomization allows us to handle the large feature space and prevents over-fitting. The Markov temporal structure provides temporally consistent decisions during testing. The proposed random forest uses a discriminative Markov decision tree, where every nonterminal node is a discriminative classifier and the Markov structure is applied at leaf nodes. The proposed approach outperforms the state-of-the-art methods on a new challenging video dataset of assembling a pump system.