An exploration with online complex activity recognition using cellphone accelerometer

We investigate the problem of online detection of complex activities (such as cooking, lunch, work at desk), i.e., recognizing them while the activities are being performed using parts of the sensor data. In contrast to prior work, where complex activity recognition is performed offline with the observation of the activity available for its entire duration and utilizing deeply-instrumented environments, we focus on online activity detection using only accelerometer data from a single body-worn smartphone device. We present window based algorithms for online detection that effectively perform different tradeoffs between classification accuracy and detection latency. We present results of our exploration using a longitudinally-extensive and clearly-annotated cellphone accelerometer data trace that captures the true-life complex activity behavior of five subjects

Semantic Activity Classification Using Locomotive Signatures from Mobile Phones

We explore the use of mobile phone-generated sensor feeds to determine the high-level (i.e., at the semantic level), indoor, lifestyle activities of individuals, such as cooking & dining at home and working & having lunch at the work- place. We propose and evaluate a 2-Tier activity extraction framework (called SAMMPLE1) where features of the low-level accelerometer data are first used to identify individual locomotive micro-activities (e.g., sitting or standing), and the micro-activity sequence is subsequently used to identify the discriminatory characteristics of individual semantic activities. Using 152 days of real-life behavioral traces from users, our approach achieves an average accuracy of 77.14%, an improvement of 16.37% from the traditional 1-Tier approach, which directly uses statistical features of the accelerometer stream, towards such activity classification tasks

Continuous Recognition of Daily Activities from Multiple Heterogeneous Sensors

日常生活行為辨識是用來達到主動服務以及自動監控的一項關鍵科技。我們希望能從一段包含未知行為的感測器資訊中，連續辨識出發生的行為與時間。在這個論文中，我們透過感測器序列的資訊，不斷的去判斷出每一個時間點發生的行為來達到連續辨識的目的。過混合多種異質的感測器有助於我們分辨出各種行為，然而，異質感測器在資訊呈現形式上往往有很大的差別。我們希望能發掘不同模型在整合這些資訊時的特性，在我們的研究中比較了不同的模型在這個問題上的適用度，包括隱藏馬可夫模型 (Hidden Markov Model)，%階層式隱藏式馬可夫模型(HHMM)，條件隨機場 (Conditional Random Field)，以及結構式支持向量機 (Structural Support Vector Machine)。驗結果說明，鑑別式模型如條件隨機場，以及結構式支持向量機對於整合感測器較為有效，其準確度明顯高於隱藏馬可夫模型。%其餘兩種模型。其中結構式支持向量機對於各種不同形式的感測器都能擁有相當好的結果。除此之外，我們引入了數種重疊特徵提取的方法，使用這些特徵值能夠進一步的改善準確度，在使用的這些特徵後，條件隨機場跟結構式支持向量機得到了相當接近的準確度。了提供主動的服務，我們比較了數種不同的即時辨識方法。在我們所比較的方法中，On-line Viterbi得到了最佳的單位時間準確度，然而卻會產生相當多不必要的服務。我們提出了Smooth On-line Viterbi方法來改善這種情形。Recognition of daily activities is an enabling technology for active service providing and automatic in-home monitoring. In this thesis, we aim to recognize activities in a long sensor stream without knowing the boundary of activities. We formulate this continuous recognition problem as a sequence labeling problem. The activity is labeled every a fixed interval given the sensor readings.using multiple heterogeneous sensors helps disambiguate different activities. However, these sensors are very diverse in readings. To evaluate the capability of models in dealing with such diverse sensors, we compare several state-of-the-art sequence labeling algorithms including hidden Markov model (HMM), linear-chain conditional random field (LCRF) and SVMhmm. The results show that the two discriminative models, LCRF and SVMhmm, significantly outperform HMM. SVM$^{hmm}$ show robustness in dealing with all sensors we used. By incorporating proper overlapping features, the accuracy can be further improved. In additions, CRF and SVMhmm perform comparably with these overlapping features.or active service providing, we evaluate various inference strategies for the on-line recognition problem. On-line Viterbi algorithm achieves highest frame accuracy but suffers from high insertion errors that may cause unexpected services. We propose smooth on-line Viterbi algorithm to solve this problem.Acknowledgments iibstract vist of Figures xiiiist of Tables xivhapter 1 Introduction 1.1 Motivation 1.2 Research Objective 3.3 Thesis Organization 5hapter 2 Related Work 7.1 Sensor Setting 7.1.1 Sensor Selection 7.1.2 Multiple Heterogeneous Sensors 12.1.3 Sensor Placement 13.2 Classification Algorithms 13.2.1 Feature Extraction 14.2.2 Classifiers 14.2.3 Generative Modeling 16.2.4 Sequence Segmentation 17.3 Sequence Models 19.3.1 Hidden Markov Model 19.3.2 Dynamic Bayesian Network 20.3.3 Maximum Entropy Markov Model and Conditional Randomield 21.3.4 Structural SVM 22hapter 3 Off-line Recognition for Monitoring 25.1 Problem Definition 25.2 E-Home Dataset 26.3 Activity Modeling 27.3.1 HMM 27.3.2 Linear Chain CRF 28.3.3 SVMhmm 30.3.4 Other Approaches 31.4 Performance Measures 31.5 Raw Features 32.5.1 Results 33.6 Overlapping Features 35.6.1 Generative Audio Probabilities 35.6.2 Region and Region Transitions 36.6.3 NextRFID and LastRFID 37.6.4 Results 38hapter 4 On-line Recognition for Active Services 41.1 Problem Definition 41.2 Dynamic Programming Algorithms 42.2.1 On-line Viterbi Algorithm 42.2.2 Bayes Filtering 43.2.3 Token Passing Algorithm 43.3 Evaluation 44hapter 5 Segment Analysis 45.1 Segment Error 46.1.1 Minimum Edit Distance 46.1.2 Time Critical Minimum Edit Distance 48.2 Evaluation 50.2.1 Off-line Recognition 50.2.2 On-line Recognition 50.3 Smooth on-line Viterbi 51.3.1 Evaluation 52hapter 6 Conclusion 57ibliography 6