Evaluation of Prompted Annotation of Activity Data Recorded from a Smart Phone

Cleland, Ian; Han, Myung-Mook; Nugent, Chris; Lee, Hosung; McClean, Sally; Zhang, Shuai; Lee, Sungyoung

doi:10.3390/s140915861

Cited by 54 publications

(41 citation statements)

References 33 publications

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“…A variety of classifiers can be used for this learning problem. Examples include decision tree [33][34][36][37], naïve Bayes [33], Bayesian net [34], support vector machine [33][34], nearest neighbor [33][20], hidden Markov model [38][39][33], conditional random field [34], and Gaussian mixture model (GMM) [38][39]. …”

Section: Reviewmentioning

confidence: 99%

A survey of methods for time series change point detection

2016

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Change points are abrupt variations in time series data. Such abrupt changes may represent transitions that occur between states. Detection of change points is useful in modelling and prediction of time series and is found in application areas such as medical condition monitoring, climate change detection, speech and image analysis, and human activity analysis. This survey article enumerates, categorizes, and compares many of the methods that have been proposed to detect change points in time series. The methods examined include both supervised and unsupervised algorithms that have been introduced and evaluated. We introduce several criteria to compare the algorithms. Finally, we present some grand challenges for the community to consider.

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Section: Reviewmentioning

confidence: 99%

A survey of methods for time series change point detection

2016

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“…When the rule about the leaving observation is considered too, conditions (7) and (9), and conditions (8) and (10) must be jointly satisfied to detect a peak or a trough, respectively. § § Motivated by the comment of a referee, we have checked the distributional assumption in case of financial time series.…”

Section: A Probabilistic Approach For Turning Point Detectionmentioning

confidence: 99%

Online detection of financial time series peaks and troughs: A probability‐based approach*

Bramante

Facchinetti

Zappa

2019

Statistical Analysis

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The problem related to the identification of a change in time series trajectories plays a crucial role in many contexts. In this paper, we propose a flexible and computationally efficient procedure for turning point identification based on hypothesis testing applied to the difference between two consecutive slopes in a rolling regression framework. Along with the description of the methodology, to measure the performance of the method we have applied it to the S&P 500 Stock Index and its subsector indices. By using an in‐sample/out‐of‐sample approach we compare results with the profit/losses we could obtain by using the moving average crossover strategy. Results show that the operating signals obtained by our proposal may better enable financial analysts to make profitable decisions. Finally we present an extensive simulation study to show the weaknesses and strengths of the proposal under different expected returns and volatility scenarios.

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“…A number of techniques have been proposed for this task. Some approaches are unsupervised and utilize object-use fingerprints [64], [65] or statistical change point detection [66], [67]. …”

Section: Related Workmentioning

confidence: 99%

Learning Activity Predictors from Sensor Data: Algorithms, Evaluation, and Applications

Minor

Doppa

Cook

2017

IEEE Trans. Knowl. Data Eng.

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Recent progress in Internet of Things (IoT) platforms has allowed us to collect large amounts of sensing data. However, there are significant challenges in converting this large-scale sensing data into decisions for real-world applications. Motivated by applications like health monitoring and intervention and home automation we consider a novel problem called Activity Prediction, where the goal is to predict future activity occurrence times from sensor data. In this paper, we make three main contributions. First, we formulate and solve the activity prediction problem in the framework of imitation learning and reduce it to a simple regression learning problem. This approach allows us to leverage powerful regression learners that can reason about the relational structure of the problem with negligible computational overhead. Second, we present several metrics to evaluate activity predictors in the context of real-world applications. Third, we evaluate our approach using real sensor data collected from 24 smart home testbeds. We also embed the learned predictor into a mobile-device-based activity prompter and evaluate the app for 9 participants living in smart homes. Our results indicate that our activity predictor performs better than the baseline methods, and offers a simple approach for predicting activities from sensor data.

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Evaluation of Prompted Annotation of Activity Data Recorded from a Smart Phone

Cited by 54 publications

References 33 publications

A survey of methods for time series change point detection

A survey of methods for time series change point detection

Online detection of financial time series peaks and troughs: A probability‐based approach*

Learning Activity Predictors from Sensor Data: Algorithms, Evaluation, and Applications

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