UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones

Micucci, Daniela; Mobilio, Marco; Napoletano, Paolo

doi:10.20944/preprints201706.0033.v2

Cited by 58 publications

(29 citation statements)

References 34 publications

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“…e sampling rate of signals varied signi cantly across phones with values between 50-200Hz. [41] contains triaxial accelerometer signals collected from a Samsung Galaxy Nexus smartphone at 50Hz. irty subjects participated in the data collection process forming a diverse sample of the population with di erent height, weight, age, and gender.…”

Section: Datasetsmentioning

confidence: 99%

Multi-task Self-Supervised Learning for Human Activity Detection

Saeed

Özçelebi

Lukkien

2019

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

229

199

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Deep learning methods are successfully used in applications pertaining to ubiquitous computing, pervasive intelligence, health, and well-being. Speci cally, the area of human activity recognition (HAR) is primarily transformed by the convolutional and recurrent neural networks, thanks to their ability to learn semantic representations directly from raw input. However, in order to extract generalizable features massive amounts of well-curated data are required, which is a notoriously challenging task; hindered by privacy issues and annotation costs. erefore, unsupervised representation learning (i.e., learning without manually labeling the instances) is of prime importance to leverage the vast amount of unlabeled data produced by smart devices. In this work, we propose a novel self-supervised technique for feature learning from sensory data that does not require access to any form of semantic labels, i.e., activity classes. We learn a multi-task temporal convolutional network to recognize transformations applied on an input signal. By exploiting these transformations, we demonstrate that simple auxiliary tasks of the binary classi cation result in a strong supervisory signal for extracting useful features for the down-stream task. We extensively evaluate the proposed approach on several publicly available datasets for smartphone-based HAR in unsupervised, semi-supervised and transfer learning se ings. Our method achieves performance levels superior to or comparable with fully-supervised networks trained directly with activity labels, and it performs signi cantly be er than unsupervised learning through autoencoders. Notably, for the semi-supervised case, the self-supervised features substantially boost the detection rate by a aining a kappa score between 0.7 − 0.8 with only 10 labeled examples per class. We get similar impressive performance even if the features are transferred from a di erent data source. Self-supervision drastically reduces the requirement of labeled activity data, e ectively narrowing the gap between supervised and unsupervised techniques for learning meaningful representations. While this paper focuses on HAR as the application domain, the proposed approach is general and could be applied to a wide variety of problems in other areas. 000:2 • A. Saeed et al.activity recognition (HAR), 1D convolutional and recurrent neural networks trained on raw labeled signals signi cantly improve the detection rate over traditional methods [20,44,68,72,73]. Despite the recent advances in the eld of HAR, learning representations from a massive amount of unlabeled data still presents a signi cant challenge. Obtaining large, well-curated activity recognition datasets is problematic due to a number of issues. First, smartphone data are privacy sensitive, which makes it hard to collect su cient amounts of user-activity instances in a real-life se ing. Second, the annotation cost and the time it takes to generate a large volume of labeled instances are prohibitive. Finally, the diversity of devices, types of embedd...

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Section: Datasetsmentioning

confidence: 99%

Multi-task Self-Supervised Learning for Human Activity Detection

Saeed

Özçelebi

Lukkien

2019

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

229

199

View full text Add to dashboard Cite

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“…To choose the optimal datasets for this study, we considered the complexity and richness of the datasets. Based on the background of our research, we selected the OPPORTUNITY [51], PAMAP2 [52] and UniMiB-SHAR [53] benchmark datasets for our experiments.…”

Section: Methodsmentioning

confidence: 99%

DFTerNet: Towards 2-bit Dynamic Fusion Networks for Accurate Human Activity Recognition

et al. 2018

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Deep Convolutional Neural Networks (DCNNs) are currently popular in human activity recognition (HAR) applications. However, in the face of modern artificial intelligence sensor-based games, many research achievements cannot be practically applied on portable devices (i.e., smart phone, VR/AR). DCNNs are typically resource-intensive and too large to be deployed on portable devices, thus this limits the practical application of complex activity detection. In addition, since portable devices do not possess high-performance Graphic Processing Units (GPUs), there is hardly any improvement in Action Game (ACT) experience. Besides, in order to deal with multi-sensor collaboration, all previous human activity recognition models typically treated the representations from different sensor signal sources equally. However, distinct types of activities should adopt different fusion strategies. In this paper, a novel scheme is proposed. This scheme is used to train 2-bit Convolutional Neural Networks with weights and activations constrained to {-0.5, 0, 0.5}. It takes into account the correlation between different sensor signal sources and the activity types. This model, which we refer to as DFTerNet, aims at producing a more reliable inference and better trade-offs for practical applications. It's known that quantization of weights and activations can substantially reduce memory size and use more efficient bitwise operations to replace floating or matrix operations to achieve much faster calculation and lower power consumption. Our basic idea is to exploit quantization of weights and activations directly in pre-trained filter banks and adopt dynamic fusion strategies for different activity types. Experiments demonstrate that by using a dynamic fusion strategy, it is possible to exceed the baseline model performance by up to ∼5% on activity recognition datasets like the OPPORTUNITY and PAMAP2 datasets. Using the quantization method proposed, we were able to achieve performances closer to that of the full-precision counterpart. These results were also verified using the UniMiB-SHAR dataset. In addition, the proposed method can achieve ∼9× acceleration on CPUs and ∼11× memory saving.

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“…The first group analyses the vital signs provided by wearable sensors (Banaee et al, 2013) such as electrocardiogram, oxygen saturation, heart rate, photoplethysmography, blood glucose, blood pressure and respiratory rate. The second group is focused on recognising and monitoring individual human activities (Liao et al, 2005;Luque et al, 2014;Vilarinho et al, 2015;Micucci et al, 2017;Kulev et al, 2016), which also overlaps with the fields of computer vision, machine learning and data mining. Our study in this paper is closer to the second group.…”

Section: Related Workmentioning

confidence: 99%

A hybrid model for predicting human physical activity status from lifelogging data

Chen

Allinson

et al. 2020

European Journal of Operational Research

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One trend in the recent healthcare transformations is people are encouraged to monitor and manage their health based on their daily diets and physical activity habits. However, much attention of the use of operational research and analytical models in healthcare has been paid to the systematic level such as country or regional policy making or organisational issues. This paper proposes a model concerned with healthcare analytics at the individual level, which can predict human physical activity status from sequential lifelogging data collected from wearable sensors. The model has a two-stage hybrid structure (in short, MOGP-HMM) -a multi-objective genetic programming (MOGP) algorithm in the first stage to reduce the dimensions of lifelogging data and a hidden Markov model (HMM) in the second stage for activity status prediction over time. It can be used as a decision support tool to provide real-time monitoring, statistical analysis and personalized advice to individuals, encouraging positive attitudes towards healthy lifestyles. We validate the model with the real data collected from a group of participants in the UK, and compare it with other popular two-stage hybrid models. Our experimental results show that the MOGP-HMM can achieve comparable performance. To the best of our knowledge, this is the very first study that uses the MOGP in the hybrid two-stage structure for individuals' activity status prediction. It fits seamlessly with the current trend in the UK healthcare transformation of patient empowerment as well as contributing to a strategic development for more efficient and cost-effective provision of healthcare.

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UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones

Cited by 58 publications

References 34 publications

Multi-task Self-Supervised Learning for Human Activity Detection

Multi-task Self-Supervised Learning for Human Activity Detection

DFTerNet: Towards 2-bit Dynamic Fusion Networks for Accurate Human Activity Recognition

A hybrid model for predicting human physical activity status from lifelogging data

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