This paper proposes a multi-level feature learning framework for human action recognition using body-worn inertial sensors. The framework consists of three phases, respectively designed to analyze signal-based (low-level), components (mid-level) and semantic (high-level) information. Low-level features, extracted from raw signals, capture the time and frequency domain property while mid-level representations, obtained through the dictionary learning method, learn the composition of the action. The Max-margin Latent Pattern Learning (MLPL) method is proposed and implemented on the concatenation of low- and mid-level features to learn high-level semantic descriptions of latent action patterns as the output of our framework. Various experiments on Opp, Skoda and WISDM datasets show that the semantic feature learned by this framework possesses higher representation ability than low- and mid-level features. Compared with existing methods, the proposed method achieves state-of-the-art performances.
@cite_53: We propose a sparse-coding framework for activity recognition in ubiquitous and mobile computing that alleviates two fundamental problems of current supervised learning approaches. (i) It automatically derives a compact, sparse and meaningful feature representation of sensor data that does not rely on prior expert knowledge and generalizes well across domain boundaries. (ii) It exploits unlabeled sample data for bootstrapping effective activity recognizers, i.e., substantially reduces the amount of ground truth annotation required for model estimation. Such unlabeled data is easy to obtain, e.g., through contemporary smartphones carried by users as they go about their everyday activities.Based on the self-taught learning paradigm we automatically derive an over-complete set of basis vectors from unlabeled data that captures inherent patterns present within activity data. Through projecting raw sensor data onto the feature space defined by such over-complete sets of basis vectors effective feature extraction is pursued. Given these learned feature representations, classification backends are then trained using small amounts of labeled training data.We study the new approach in detail using two datasets which differ in terms of the recognition tasks and sensor modalities. Primarily we focus on a transportation mode analysis task, a popular task in mobile-phone based sensing. The sparse-coding framework demonstrates better performance than the state-of-the-art in supervised learning approaches. More importantly, we show the practical potential of the new approach by successfully evaluating its generalization capabilities across both domain and sensor modalities by considering the popular Opportunity dataset. Our feature learning approach outperforms state-of-the-art approaches to analyzing activities of daily living.
@cite_1: In activity recognition, traditionally, features are chosen heuristically, based on explicit domain knowledge. Typical features are statistical measures, like mean, standard deviation, etc., which are tailored to the application at hand and might not fit in other cases. However, Feature Learning techniques have recently gained attention for building approaches that generalize over different application domains. More conventional approaches, like Principal Component Analysis, and newer ones, like Deep Belief Networks, have been studied so far and yielded significantly better results than traditional techniques. In this paper we study the potential of Shift-invariant Sparse Coding (SISC) as an additional Feature Learning technique for activity recognition. We evaluate the performance on several publicly available activity recognition data sets and show that classification based on features learned by SISC outperforms other previously presented Feature Learning techniques.
@cite_52: High-level and longer-term activity recognition has great potentials in areas such as medical diagnosis and human behavior modeling. So far however, activity recognition research has mostly focused on lowlevel and short-term activities. This paper therefore makes a first step towards recognition of high-level activities as they occur in daily life. For this we record a realistic 10h data set and analyze the performance of four different algorithms for the recognition of both low- and high-level activities. Here we focus on simple features and computationally efficient algorithms as this facilitates the embedding and deployment of the approach in real-world scenarios. While preliminary, the experimental results suggest that the recognition of high-level activities can be achieved with the same algorithms as the recognition of low-level activities.
@cite_49: Human activity modeling and recognition using wearable sensors is important in pervasive healthcare, with applications including quantitative assessment of motor function, rehabilitation, and elder care. Previous human activity recognition techniques use a "whole-motion" model in which continuous sensor streams are divided into windows with a fixed time duration whose length is chosen such that all the relevant information in each activity signal can be extracted from each window. In this paper, we present a statistical motion primitive-based framework for human activity representation and recognition. Our framework is based on Bag-of-Features (BoF), which builds activity models using histograms of primitive symbols. We experimentally validate the effectiveness the BoF-based framework for recognizing nine activity classes and evaluate six factors which impact the performance of the framework. The factors include window size, choices of features, methods to construct motion primitives, motion vocabulary size, weighting schemes of motion primitive assignments, and learning machine kernel functions. Finally, we demonstrate that our statistical BoF-based framework can achieve much better performance compared to a non-statistical string-matching-based approach.
@cite_31: This paper explores the possibility of using low-level activity spotting for daily routine recognition. Using occurrence statistics of low-level activities and simple classifiers based on their statistics allows to train a discriminative classifier for daily routine activities such as working and commuting. Using a recently published data set we find that the number of required low-level activities is surprisingly low, thus, enabling efficient algorithms for daily routine recognition through low-level activity spotting. More specifically we employ the JointBoosting-framework using low-level activity spotters as weak classiers. By using certain low-level activities as support, we achieve an overall recall rate of over 90 and precision rate of over 88 . Tuning down the weak classifiers using only 2.61 of the original data still yields recall and precision rates of 80 and 83 .
Mid-level features are generally extracted from the low-level ones to explore the components and structural information of signals. They are prevalent in HAR tasks for robustness against noise and discrimination in representations @cite_53 @cite_1 . @cite_52 and @cite_49 implemented the bag-of-words (BOW) model to obtain statistical descriptions of motion primitives. Their works showed the effectiveness of the BOW model in sensor-based action recognition tasks. @cite_31 extracted the occurrence statistics feature from low-level actions in a way that is similar to @cite_52 and then implemented the JointBoosting-framework. One characteristic of their method was to adopt a top-down perspective, using a feature selection algorithm to learn the distinctive motion primitives from the labeled high-level action. @cite_53 and @cite_1 both utilized sparse coding and adopted the convolution basis, which could resist shifts in time and thus reduce redundancy of basis. Our work is similar to @cite_49 , in which the mid-level representation is achieved through hard coding and occurrence statistics.