Smartphone-Based Solutions for Fall Detection and Prevention: Challenges and Open Issues

Habib, Mohammad Ashfak; Mohktar, Mas S.; Kamaruzzaman, Shahrul Bahyah; Lim, Kheng Seang; Tan, Maw Pin; Ibrahim, Fatimah

doi:10.3390/s140407181

Cited by 184 publications

(113 citation statements)

References 90 publications

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“…Threshold-based classification is still the most widely used strategy for fall detection, as it is less computationally intensive than support vector machines and similar classification algorithms [11]. We analyzed two widely used alternatives: Threshold 1 (T 1 ) which follows maximum accuracy, and Threshold 2 (T 2 ) which maximizes the sensitivity (fall detection capability).…”

Section: Classificationmentioning

confidence: 99%

“…In both cases, the preferred sensor is the triaxial accelerometer because of its low cost, small size, and because it is built-in in almost all smartphones [6]. Smartphones are a popular selection for authors because they include a robust hardware, a powerful processor, and they are economically affordable [6,11]. However, the low cost of the individual components and design tools has encouraged authors to develop their own embedded devices too [13].…”

Section: Introductionmentioning

confidence: 99%

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SisFall: A Fall and Movement Dataset

Sucerquia

López

Vargas-Bonilla

2016

Preprint

112

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Abstract:Research on fall and movement detection with wearable devices has witnessed promising growth. However, there are few publicly available datasets, all recorded with smartphones, which are insufficient for testing new proposals due to their absence of objective population, lack of performed activities, and limited information. Here, we present a dataset of falls and activities of daily living (ADLs) acquired with a self-developed device composed of two types of accelerometer and one gyroscope. It consists of 19 ADLs and 15 fall types performed by 23 young adults, 15 ADL types performed by 14 healthy and independent participants over 62 years old, and data from one participant of 60 years old that performed all ADLs and falls. These activities were selected based on a survey and a literature analysis. We test the dataset with widely used feature extraction and a simple to implement threshold based classification, achieving up to 96% of accuracy in fall detection. An individual activity analysis demonstrates that most errors coincide in a few number of activities where new approaches could be focused. Finally, validation tests with elderly people significantly reduced the fall detection performance of the tested features. This validates findings of other authors and encourages developing new strategies with this new dataset as the benchmark.

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Section: Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SisFall: A Fall and Movement Dataset

Sucerquia

López

Vargas-Bonilla

2016

Preprint

112

View full text Add to dashboard Cite

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“…Furthermore, the low adoption barrier on healthcare applications [28] through application markets such as Google Play or AppStore makes them the best option to target the mass market. Some of them are focused on fall detection [29,30], but normally do not cover both ADL and falls [31], so a classification system must be designed to consider them.…”

Section: Activity Recognition Systems For Eldersmentioning

confidence: 99%

Mobile activity recognition and fall detection system for elderly people using Ameva algorithm

Concepción

Morillo

Álvarez-García

et al. 2017

Pervasive and Mobile Computing

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a b s t r a c tCurrently, the lifestyle of elderly people is regularly monitored in order to establish guidelines for rehabilitation processes or ensure the welfare of this segment of the population. In this sense, activity recognition is essential to detect an objective set of behaviors throughout the day. This paper describes an accurate, comfortable and efficient system, which monitors the physical activity carried out by the user. An extension to an awarded activity recognition system that participated in the EvAAL 2012 and EvAAL 2013 competitions is presented. This approach uses data retrieved from accelerometer sensors to generate discrete variables and it is tested in a non-controlled environment. In order to achieve the goal, the core of the algorithm Ameva is used to develop an innovative selection, discretization and classification technique for activity recognition. Moreover, with the purpose of reducing the cost and increasing user acceptance and usability, the entire system uses only a smartphone to recover all the information required.

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“…An automatic fall detection system which does not need user intervention can overcome this problem. Because of the importance of this issue, a lot of research has been done to solve the fall detection challenge, as can be seen in the numerous available review articles [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. There are different ways to detect a fall; Mubashir et al categorizes them, for example, in three categories: wearable sensors, vision, and ambient/fusion [14].…”

Section: Introductionmentioning

confidence: 99%

Three Ways to Improve the Performance of Real-Life Camera-Based Fall Detection Systems

et al. 2017

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More than thirty percent of persons over 65 years fall at least once a year and are often not able to get up again. Camera-based fall detection systems can help by triggering an alarm when falls occur. Previously we showed that real-life data poses significant challenges, resulting in high false alarm rates. Here, we show three ways to tackle this. First, using a particle filter combined with a person detector increases the robustness of our foreground segmentation, reducing the number of false alarms by 50%. Second, selecting only nonoccluded falls for training further decreases the false alarm rate on average from 31.4 to 26 falls per day. But, most importantly, this improvement is also shown by the doubling of the AUC of the precision-recall curve compared to using all falls. Third, personalizing the detector by adding several days containing only normal activities, no fall incidents, of the monitored person to the training data further increases the robustness of our fall detection system. In one case, this reduced the number of false alarms by a factor of 7 while in another one the sensitivity increased by 17% for an increase of the false alarms of 11%.

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Smartphone-Based Solutions for Fall Detection and Prevention: Challenges and Open Issues

Cited by 184 publications

References 90 publications

SisFall: A Fall and Movement Dataset

SisFall: A Fall and Movement Dataset

Mobile activity recognition and fall detection system for elderly people using Ameva algorithm

Three Ways to Improve the Performance of Real-Life Camera-Based Fall Detection Systems

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