Activity recognition through interactive machine learning in a dynamic sensor setting

Tegen, Agnes; Davidsson, Paul; Persson, Jan

doi:10.1007/s00779-020-01414-2

Cited by 6 publications

(9 citation statements)

References 31 publications

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“…It is the two machine teaching strategies MT triggered by error and MT triggered by state change that generally perform best in the experiments. The results are in line with our previous work [16,17] and give an indication that letting the user be more proactive can be beneficial on performance. This is noteworthy since most interactive online machine learning strategies employed in the literature fall under the active learning subcategory.…”

Section: Resultssupporting

confidence: 91%

A Taxonomy of Interactive Online Machine Learning Strategies

Tegen

Davidsson

Persson

2021

Machine Learning and Knowledge Discovery in Databases

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In interactive machine learning, human users and learning algorithms work together in order to solve challenging learning problems, e.g. with limited or no annotated data or trust issues. As annotating data can be costly, it is important to minimize the amount of annotated data needed for training while still getting a high classification accuracy. This is done by attempting to select the most informative data instances for training, where the amount of instances is limited by a labelling budget. In an online learning setting, the decision of whether or not to select an instance for labelling has to be done on-the-fly, as the data arrives in a sequential order and is only valid for a limited time period. We present a taxonomy of interactive online machine learning strategies. An interactive learning strategy determines which instances to label in an unlabelled dataset. In the taxonomy we differentiate between interactive learning strategies when the computer controls the learning process (active learning) and those when human users control the learning process (machine teaching). We then make a distinction between what triggers the learning: active learning could be triggered by uncertainty, time, or randomly, whereas machine teaching could be triggered by errors, state changes, time, or factors related to the user. We also illustrate the taxonomy by implementing versions of the different strategies and performing experiments on a benchmark dataset as well as on a synthetically generated dataset. The results show that the choice of interactive learning strategy affects performance, especially in the beginning of the online learning process, when there is a limited amount of labelled data.

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Section: Resultssupporting

confidence: 91%

A Taxonomy of Interactive Online Machine Learning Strategies

Tegen

Davidsson

Persson

2021

Machine Learning and Knowledge Discovery in Databases

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“…This method combines machine learning and DL methods based on internal parameters. The validation process step is not required in the feature selection process [ 27 , 77 ].…”

Section: Har Analysismentioning

confidence: 99%

“…Initial training should be done so that a new activity performed after training should be correctly identified, so in the laboratory environment, learning processes should be done carefully. Therefore, HARS should be in a controlled environment under an experienced team's supervision and a standard and public dataset [ 77 ]. As mentioned earlier, in activity recognition in smart homes, we see regular activity to monitor healthcare and find changes in people's patterns and lifestyles [ 81 ].…”

Section: Har Analysismentioning

confidence: 99%

Wearable Sensor-Based Human Activity Recognition in the Smart Healthcare System

Serpush

Menhaj

Masoumi

et al. 2022

Computational Intelligence and Neuroscience

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Human activity recognition (HAR) has been of interest in recent years due to the growing demands in many areas. Applications of HAR include healthcare systems to monitor activities of daily living (ADL) (primarily due to the rapidly growing population of the elderly), security environments for automatic recognition of abnormal activities to notify the relevant authorities, and improve human interaction with the computer. HAR research can be classified according to the data acquisition tools (sensors or cameras), methods (handcrafted methods or deep learning methods), and the complexity of the activity. In the healthcare system, HAR based on wearable sensors is a new technology that consists of three essential parts worth examining: the location of the wearable sensor, data preprocessing (feature calculation, extraction, and selection), and the recognition methods. This survey aims to examine all aspects of HAR based on wearable sensors, thus analyzing the applications, challenges, datasets, approaches, and components. It also provides coherent categorizations, purposeful comparisons, and systematic architecture. Then, this paper performs qualitative evaluations by criteria considered in this system on the approaches and makes available comprehensive reviews of the HAR system. Therefore, this survey is more extensive and coherent than recent surveys in this field.

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“…This approach is well-motivated in many modern ML problems, in particular, when labels are complicated, time-consuming, or expensive to collect [12]. Also studies have highlighted continued research on activity recognition techniques based on interactive machine learning, for example in dynamic sensor environment where streaming data is able to be used to measure accuracy [13,14] [9].…”

Section: B Related Workmentioning

confidence: 99%

Active Machine Learning Adversarial Attack Detection in the User Feedback Process

et al. 2021

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Modern Information and Communication Technology (ICT)-based applications utilize current technological advancements for purposes of streaming data, as a way of adapting to the ever-changing technological landscape. Such efforts require providing accurate, meaningful, and trustworthy output from the streaming sensors particularly during dynamic virtual sensing. However, to ensure that the sensing ecosystem is devoid of any sensor threats or active attacks, it is paramount to implement secure real-time strategies. Fundamentally, real-time detection of adversarial attacks/instances during the User Feedback Process (UFP) is the key to forecasting potential attacks in active learning. Also, according to existing literature, there lacks a comprehensive study that has a focus on adversarial detection from an active machine learning perspective at the time of writing this paper. Therefore, the authors posit the importance of detecting adversarial attacks in active learning strategy. Attack in the context of this paper through a UFP-Threat driven model has been presented as any action that exerts an alteration to the learning system or data. To achieve this, the study employed ambient data collected from a smart environment human activity recognition from (Continuous Ambient Sensors Dataset, CASA) with fully labeled connections, where we intentionally subject the Dataset to wrong labels as a targeted/manipulative attack (by a malevolent labeler) in the UFP, with an assumption that the user-labels were connected to unique identities. While the dataset's focus is to classify tasks and predict activities, our study gives a focus on active adversarial strategies from an information security point of view. Furthermore, the strategies for modeling threats have been presented using the Meta Attack Language (MAL) compiler for purposes adversarial detection. The findings from the experiments conducted have shown that real-time adversarial identification and profiling during the UFP could significantly increase the accuracy during the learning process with a high degree of certainty and paves the way towards an automated adversarial detection and profiling approaches on the Internet of Cognitive Things (ICoT).

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Activity recognition through interactive machine learning in a dynamic sensor setting

Cited by 6 publications

References 31 publications

A Taxonomy of Interactive Online Machine Learning Strategies

A Taxonomy of Interactive Online Machine Learning Strategies

Wearable Sensor-Based Human Activity Recognition in the Smart Healthcare System

Active Machine Learning Adversarial Attack Detection in the User Feedback Process

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