Personalization and user verification in wearable systems using biometric walking patterns

Casale, Pierluigi; Pujol, Oriol; Radeva, Petia

doi:10.1007/s00779-011-0415-z

“…This set includes, sorted by year of creation from the oldest the most recent, the following datasets: DLR v2 [22], USC HAD [23], DaLiAc [10], EvAAL [24], UCI ARSA [25], BaSA [26], MMsys [9], and SisFall [27].…”

Section: Adls and Fallsmentioning

confidence: 99%

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

Micucci¹,

Mobilio²,

Napoletano³

2017

Preprint

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Smartphones, smartwatches, fitness trackers, and ad-hoc wearable devices are being increasingly used to monitor human activities. Data acquired by the hosted sensors are usually processed by machine-learning-based algorithms to classify human activities. The success of those algorithms mostly depends on the availability of training (labeled) data that, if made publicly available, would allow researchers to make objective comparisons between techniques. Nowadays, publicly available data sets are few, often contain samples from subjects with too similar characteristics, and very often lack of specific information so that is not possible to select subsets of samples according to specific criteria. In this article, we present a new smartphone accelerometer dataset designed for activity recognition. The dataset includes 11,771 activities performed by 30 subjects of ages ranging from 18 to 60 years. Activities are divided in 17 fine grained classes grouped in two coarse grained classes: 9 types of activities of daily living (ADL) and 8 types of falls. The dataset has been stored to include all the information useful to select samples according to different criteria, such as the type of ADL performed, the age, the gender, and so on. Finally, the dataset has been benchmarked with two different classifiers and with different configurations. The best results are achieved with k-NN classifying ADLs only, considering personalization, and with both windows of 51 and 151 samples.

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“…After collecting data from the smart terminals, data holders (manufacturers) of wearable devices are willing to share the data with application developers to enrich their services or obtain monetary benefits. Typically, the data collected by these devices contain abundant privacy information [3,4]. In addition, when sharing the data recorded by human-carried wearable sensors, some personal information, such as age, height, and weight, may also be submitted under warrant [5].…”

Section: Introductionmentioning

confidence: 99%

A Clustering K-Anonymity Privacy-Preserving Method for Wearable IoT Devices

Liu

¹

,

Li

²

2018

Security and Communication Networks

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Wearable technology is one of the greatest applications of the Internet of Things. The popularity of wearable devices has led to a massive scale of personal (user-specific) data. Generally, data holders (manufacturers) of wearable devices are willing to share these data with others to get benefits. However, significant privacy concerns would arise when sharing the data with the third party in an improper manner. In this paper, we first propose a specific threat model about the data sharing process of wearable devices' data. Then we propose a -anonymity method based on clustering to preserve privacy of wearable IoT devices' data and guarantee the usability of the collected data. Experiment results demonstrate the effectiveness of the proposed method.

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“…As discussed in Section 1, we considered the datasets acquired from 2012 to be compliant with the year of the older smartphone-based dataset. This set includes, sorted by year of creation from the oldest the most recent, the following datasets: DLR v2 [37], Ugulino [38], USC HAD [39], DaLiAc [10], EvAAL [40], MHEALTH [41], UCI ARSA [32], BaSA [42], UR Fall Detection [43], MMsys [9], SisFall [44], UMA Fall (UMA Fall contains samples from both smartphones and ad-hoc wearable devices.) [23], and REALDISP [45].…”

Section: Adls and Fallsmentioning

confidence: 99%

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

Micucci

¹

,

Mobilio

²

,

Napoletano

³

2017

View full text Add to dashboard Cite

Smartphones, smartwatches, fitness trackers, and ad-hoc wearable devices are being increasingly used to monitor human activities. Data acquired by the hosted sensors are usually processed by machine-learning-based algorithms to classify human activities. The success of those algorithms mostly depends on the availability of training (labeled) data that, if made publicly available, would allow researchers to make objective comparisons between techniques. Nowadays, there are only a few publicly available data sets, which often contain samples from subjects with too similar characteristics, and very often lack specific information so that is not possible to select subsets of samples according to specific criteria. In this article, we present a new dataset of acceleration samples acquired with an Android smartphone designed for human activity recognition and fall detection. The dataset includes 11,771 samples of both human activities and falls performed by 30 subjects of ages ranging from 18 to 60 years. Samples are divided in 17 fine grained classes grouped in two coarse grained classes: one containing samples of 9 types of activities of daily living (ADL) and the other containing samples of 8 types of falls. The dataset has been stored to include all the information useful to select samples according to different criteria, such as the type of ADL performed, the age, the gender, and so on. Finally, the dataset has been benchmarked with four different classifiers and with two different feature vectors. We evaluated four different classification tasks: fall vs. no fall, 9 activities, 8 falls, 17 activities and falls. For each classification task, we performed a 5-fold cross-validation (i.e., including samples from all the subjects in both the training and the test dataset) and a leave-one-subject-out cross-validation (i.e., the test data include the samples of a subject only, and the training data, the samples of all the other subjects). Regarding the classification tasks, the major findings can be summarized as follows: (i) it is quite easy to distinguish between falls and ADLs, regardless of the classifier and the feature vector selected. Indeed, these classes of activities present quite different acceleration shapes that simplify the recognition task; (ii) on average, it is more difficult to distinguish between types of falls than between types of activities, regardless of the classifier and the feature vector selected. This is due to the similarity between the acceleration shapes of different kinds of falls. On the contrary, ADLs acceleration shapes present differences except for a small group. Finally, the evaluation shows that the presence of samples of the same subject both in the training and in the test datasets, increases the performance of the classifiers regardless of the feature vector used. This happens because each human subject differs from other subjects in performing activities even if she shares with them the same physical characteristics.

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Personalization and user verification in wearable systems using biometric walking patterns

Cited by 160 publications

References 14 publications

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

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

A Clustering K-Anonymity Privacy-Preserving Method for Wearable IoT Devices

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

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