Comparison of real-life accidental falls in older people with experimental falls in middle-aged test subjects

Kangas, Maarit; Vikman, Irene; Nyberg, Lars; Korpelainen, Raija; Lindblom, Julius; Jämsä, Timo

doi:10.1016/j.gaitpost.2011.11.016

“…Finally, studies on this topic confirm the activities we selected are common in real-life [16,[29][30][31].…”

Section: Of 14mentioning

confidence: 54%

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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“…However, self-initiated, intentional falls may differ from sudden, unexpected falls [21]. Recent reports have indicated that intentional falls in a laboratory environment have both similar and different features compared with real-life falls among older people [22,23], thus indicating the importance of evaluating fall detection systems in real-life conditions. …”

Section: Introductionmentioning

confidence: 99%

Sensitivity and False Alarm Rate of a Fall Sensor in Long-Term Fall Detection in the Elderly

Kangas

¹

,

Korpelainen²,

Vikman

³

et al. 2014

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Background: About a third of home-dwelling older people fall each year, and institutionalized older people even report a two- or threefold higher rate for falling. Automatic fall detection systems have been developed to support the independent and secure living of the elderly. Even though good fall detection sensitivity and specificity in laboratory settings have been reported, knowledge about the sensitivity and specificity of these systems in real-life conditions is still lacking. Objective: The aim of this study was to evaluate the long-term fall detection sensitivity and false alarm rate of a fall detection prototype in real-life use. Methods: A total of 15,500 h of real-life data from 16 older people, including both fallers and nonfallers, were monitored using an accelerometry-based sensor system with an implemented fall detection algorithm. Results: The fall detection system detected 12 out of 15 real-life falls, having a sensitivity of 80.0%, with a false alarm rate of 0.049 alarms per usage hour with the implemented real-time system. With minor modification of data analysis the false alarm rate was reduced to 0.025 false alarms per hour, equating to 1 false fall alarm per 40 usage hours. Conclusion: These data suggest that automatic accelerometric fall detection systems might offer a tool for improving safety among older people.

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“…Finally, studies on this topic confirm the falls we selected are common in real-life [29,[48][49][50]. …”

Section: Adls and Fallsmentioning

confidence: 64%

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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Comparison of real-life accidental falls in older people with experimental falls in middle-aged test subjects

Cited by 119 publications

References 30 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

Sensitivity and False Alarm Rate of a Fall Sensor in Long-Term Fall Detection in the Elderly

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

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