Human Activity Recognition Using Recurrent Neural Networks

Singh, Deepika; Merdivan, Erinç; Psychoula, Ismini; Kropf, Johannes; Hanke, Sten; Geist, Matthieu; Holzinger, Andreas

doi:10.1007/978-3-319-66808-6_18

Cited by 157 publications

(81 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Deep neural networks (DNN), particularly convolutional neural networks (CNN) and recurrent neural networks (RNN) have been demonstrated to be applicable to a wide range of practical problems, from image recognition (Simonyan & Zisserman, ) and image classification (Esteva et al, ) to movement recognition (Singh et al, ). At the same time these approaches are also remarkable from a scientific point of view, since they reflect human processes.…”

Section: General Approaches Of Explainable Ai Modelsmentioning

confidence: 99%

Causability and explainability of artificial intelligence in medicine

Holzinger

Langs

Denk

et al. 2019

WIREs Data Min & Knowl

Self Cite

953

571

View full text Add to dashboard Cite

Explainable artificial intelligence (AI) is attracting much interest in medicine. Technically, the problem of explainability is as old as AI itself and classic AI represented comprehensible retraceable approaches. However, their weakness was in dealing with uncertainties of the real world. Through the introduction of probabilistic learning, applications became increasingly successful, but increasingly opaque. Explainable AI deals with the implementation of transparency and traceability of statistical black‐box machine learning methods, particularly deep learning (DL). We argue that there is a need to go beyond explainable AI. To reach a level of explainable medicine we need causability. In the same way that usability encompasses measurements for the quality of use, causability encompasses measurements for the quality of explanations. In this article, we provide some necessary definitions to discriminate between explainability and causability as well as a use‐case of DL interpretation and of human explanation in histopathology. The main contribution of this article is the notion of causability, which is differentiated from explainability in that causability is a property of a person, while explainability is a property of a system This article is categorized under: Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction

show abstract

Section: General Approaches Of Explainable Ai Modelsmentioning

confidence: 99%

Causability and explainability of artificial intelligence in medicine

Holzinger

Langs

Denk

et al. 2019

WIREs Data Min & Knowl

Self Cite

953

571

View full text Add to dashboard Cite

show abstract

“…Some data sets have a very small training set size. The data sets are collected from different application domains and can be divided into seven categories as Image Outline (29), Sensor Readings (16), Motion Capture (14), Spectrographs (7), ECG measurements (7) Electric device profiles (6) and Simulated Data (6), the numbers in bracket represents the numbers of data sets in the said category.…”

Section: Dataset Usedmentioning

confidence: 99%

“…This high volume of time series data need to be analysed for meaningful use of the data. Classification of time series is an important task among time series analysis [1] which has many important applications ranging from biometric authentication such as on line signature verification [2] to electroencephalogram (EEG), electrocardiogram (ECG) analysis in medical or health care field [3] or stock price, exchange rate in financial applications [4] to human activity recognition [5,6].…”

Section: Introductionmentioning

confidence: 99%

Effect of Data Representation for Time Series Classification—A Comparative Study and a New Proposal

Nakano

Chakraborty

2019

MAKE

View full text Add to dashboard Cite

Time series classification (TSC) is becoming very important in the area of pattern recognition with the increased availability of time series data in various natural and real life phenomena. TSC is a challenging problem because, due to the attributes being ordered, traditional machine learning algorithms for static data are not quite suitable for processing temporal data. Due to the gradual increase of computing power, a large number of TSC algorithms have been developed recently. In addition to traditional feature-based, model-based or distance-based algorithms, ensemble and deep networks have recently become popular for time series classification. Time series are essentially huge, and classifying raw data is computationally expensive in terms of both processing and storage. Representation techniques for data reduction and ease of visualization are needed for accurate classification. In this work a recurrence plot-based data representation is proposed and time series classification in conjunction with a deep neural network-based classifier has been studied. A simulation experiment with 85 benchmark data sets from UCR repository has been undertaken with several state of the art algorithms for time series classification in addition to our proposed scheme of classification for comparative study. It was found that, among non-ensemble algorithms, the proposed algorithm produces the highest classification accuracy for most of the data sets.

show abstract

“…The LSTM deep neural network has been widely used for human activity recognition. [40][41][42] An LSTM layer is a recurrent neural network (RNN) layer, which supports time and data series in the network. The greatest advantage of the RNNs is their capability to take contextual information into consideration when mapping between input and output sequences through hidden layer-units.…”

Section: Vertical Displacement Activities and Floor Level Estimationmentioning

confidence: 99%

Integration of positioning and activity context information for lifelog in urban city area

Kamiya

et al. 2019

NAVIGATION

View full text Add to dashboard Cite

Smartphone‐based Lifelog (automatically annotating the users' daily experience from multisensory streams on smartphones) is in great need. Accurate positioning under any situation is one of the most significant techniques for a desirable Lifelog. This paper proposes to detect location‐related activities and use the activity information to improve positioning accuracy. In the proposed system, a human activity recognition module is developed to extract location‐related activities from multisensory streams of smartphones. After that, the proposed system integrates activity information with PDR‐based positioning results in a context‐based map‐matching framework. The developed system can be used for both outdoor and indoor scenarios. Moreover, the developed indoor positioning method is used to determine the positions of calibration points automatically in an auto‐calibration Wi‐Fi positioning system. The proposed methods achieve 3.1‐m accuracy in outdoor and average 2.2‐m accuracy in indoor situations.

show abstract

Human Activity Recognition Using Recurrent Neural Networks

Cited by 157 publications

References 22 publications

Causability and explainability of artificial intelligence in medicine

Causability and explainability of artificial intelligence in medicine

Effect of Data Representation for Time Series Classification—A Comparative Study and a New Proposal

Integration of positioning and activity context information for lifelog in urban city area

Contact Info

Product

Resources

About