Comparison and Characterization of Android-Based Fall Detection Systems

Luque, Rafael Marcos; Casilari, E.; Morón, M. J.; Redondo, Gema

doi:10.3390/s141018543

Cited by 81 publications

(55 citation statements)

References 76 publications

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“…Moreover, studies demonstrated that samples from smartphones sensors (e.g., accelerometer and gyroscope) are accurate enough to be used in clinical domain, such as ADLs recognition [22]. This is also confirmed by the amount of publications that rely on the use of smartphones as acquisition devices for fall detection systems [18,23] and ADLs recognition.…”

Section: Introductionmentioning

confidence: 69%

“…Recently, a lot of attention has been paid to wearable sensors because they are less intrusive, work outdoors, and often cheaper than the ambient ones. This is confirmed by the increasing number of techniques that are based on wearable sensors (see for example the survey by Luque et al related to fall detection techniques relying on data from smartphones [18]). …”

Section: Introductionmentioning

confidence: 81%

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

2017

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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, 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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Section: Introductionmentioning

confidence: 69%

Section: Introductionmentioning

confidence: 81%

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

2017

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Section: Introductionmentioning

confidence: 81%

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 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 subject-dependent (5-fold cross validation) and a subject-independent (leave-subject-out) evaluation. The major findings of the evaluation are the following: i) it is more difficult to distinguish between types of falls than types of activities; ii) subject-dependent evaluation outperforms the subject-independent one. The database will allow researchers to work to more robust features and classification schemes that permit to deal with different types of falls and with effect of data personalization.

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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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Comparison and Characterization of Android-Based Fall Detection Systems

Cited by 81 publications

References 76 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

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

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

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