Multimodal sensor-based fall detection within the domestic environment of elderly people

Feldwieser, Florian; Gietzelt, Matthias; Goevercin, Mehmet; Marschollek, Michael; Meis, Markus; Winkelbach, Simon; Wolf, Klaus-Hendrik; Spehr, Jens; Steinhagen‐Thiessen, Elisabeth

doi:10.1007/s00391-014-0805-8

Cited by 20 publications

(27 citation statements)

References 23 publications

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“…We experienced the reality of living with a high risk of falling, rather than simply “observed” it and support the assertion that a subjective perspective in research is valuable and increases “the knowledge yield” [ 21 ]: we gained more insight than we could have through observing, evaluating, or questioning the participants anywhere other than at home. As in previous studies [ 15 ] we had some technical issues (with equipment failure and obscured sensors) and participants disclosed some concerns about surveillance but every insight gained at this stage will inform a programme of research that is now based on experience rather than supposition.…”

Section: Discussionmentioning

confidence: 92%

“…Deciding where to position the minimum number of sensors capable of capturing useful data, unobtrusively, in appropriate locations requires consideration. When Feldwieser et al [ 15 ] trialled a fall-detection system in elderly people's homes, 15 falls occurred (over 1000-plus measurement days) but none within range of the Kinect sensor installed; algorithms falsely detected multiple falls every day (4592 in total); and the participants' acceptance of technology they considered “generally useful” before installation decreased with experience. To avoid some of these unwanted outcomes, we proposed a qualitative study to initiate our programme of research.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Could In-Home Sensors Surpass Human Observation of People with Parkinson’s at High Risk of Falling? An Ethnographic Study

Stack

King

Janko

et al. 2016

BioMed Research International

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Self-report underpins our understanding of falls among people with Parkinson's (PwP) as they largely happen unwitnessed at home. In this qualitative study, we used an ethnographic approach to investigate which in-home sensors, in which locations, could gather useful data about fall risk. Over six weeks, we observed five independently mobile PwP at high risk of falling, at home. We made field notes about falls (prior events and concerns) and recorded movement with video, Kinect, and wearable sensors. The three women and two men (aged 71 to 79 years) having moderate or severe Parkinson's were dependent on others and highly sedentary. We most commonly noted balance protection, loss, and restoration during chair transfers, walks across open spaces and through gaps, turns, steps up and down, and tasks in standing (all evident walking between chair and stairs, e.g.). Our unobtrusive sensors were acceptable to participants: they could detect instability during everyday activity at home and potentially guide intervention. Monitoring the route between chair and stairs is likely to give information without invading the privacy of people at high risk of falling, with very limited mobility, who spend most of the day in their sitting rooms.

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Section: Discussionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Could In-Home Sensors Surpass Human Observation of People with Parkinson’s at High Risk of Falling? An Ethnographic Study

Stack

King

Janko

et al. 2016

BioMed Research International

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“…Soto-Mendoza et al [ 16 ] proposed a scheduling system for improving the lives of the elderly. Feldwieser et al [ 17 ] conducted a clinical study to detect falls and analyzed their effects and consequences with the help of a fall protocol using an accelerometer as well as optic and acoustic sensors. Kansiz et al [ 18 ] evaluated the time-domain features to determine the most discriminative features from supervised learning methods for fall detection.…”

Section: Introductionmentioning

confidence: 99%

Fall Detection System for the Elderly Based on the Classification of Shimmer Sensor Prototype Data

et al. 2017

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ObjectivesFalling in the elderly is considered a major cause of death. In recent years, ambient and wireless sensor platforms have been extensively used in developed countries for the detection of falls in the elderly. However, we believe extra efforts are required to address this issue in developing countries, such as Pakistan, where most deaths due to falls are not even reported. Considering this, in this paper, we propose a fall detection system prototype that s based on the classification on real time shimmer sensor data.MethodsWe first developed a data set, ‘SMotion’ of certain postures that could lead to falls in the elderly by using a body area network of Shimmer sensors and categorized the items in this data set into age and weight groups. We developed a feature selection and classification system using three classifiers, namely, support vector machine (SVM), K-nearest neighbor (KNN), and neural network (NN). Finally, a prototype was fabricated to generate alerts to caregivers, health experts, or emergency services in case of fall.ResultsTo evaluate the proposed system, SVM, KNN, and NN were used. The results of this study identified KNN as the most accurate classifier with maximum accuracy of 96% for age groups and 93% for weight groups.ConclusionsIn this paper, a classification-based fall detection system is proposed. For this purpose, the SMotion data set was developed and categorized into two groups (age and weight groups). The proposed fall detection system for the elderly is implemented through a body area sensor network using third-generation sensors. The evaluation results demonstrate the reasonable performance of the proposed fall detection prototype system in the tested scenarios.

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“…In Kangas et al [ 13 ], one of the threshold-based algorithms that was in [ 9 ] was evaluated on 15 real-world falls. Finally, 12 and eight real-world falls were considered in Feldwieser et al [ 14 ] and Bloch et al [ 15 ], respectively. In addition to these, two other studies considered real-world falls for descriptive purposes.…”

Section: Introductionmentioning

confidence: 99%

“…These studies have shown that algorithms tuned on simulated falls tend to underperform in real-life situations [ 9 , 13 , 14 ] and that simulated falls can show differences (although they also share common characteristics) with respect to real-world falls [ 16 , 17 ]. Therefore, in the present study, real-world falls and real-world ADLs are used to evaluate the performance of the proposed wavelet-based approach.…”

Section: Introductionmentioning

confidence: 99%

A Wavelet-Based Approach to Fall Detection

Palmerini

Bagalà

Zanetti

et al. 2015

Sensors

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Falls among older people are a widely documented public health problem. Automatic fall detection has recently gained huge importance because it could allow for the immediate communication of falls to medical assistance. The aim of this work is to present a novel wavelet-based approach to fall detection, focusing on the impact phase and using a dataset of real-world falls. Since recorded falls result in a non-stationary signal, a wavelet transform was chosen to examine fall patterns. The idea is to consider the average fall pattern as the “prototype fall”.In order to detect falls, every acceleration signal can be compared to this prototype through wavelet analysis. The similarity of the recorded signal with the prototype fall is a feature that can be used in order to determine the difference between falls and daily activities. The discriminative ability of this feature is evaluated on real-world data. It outperforms other features that are commonly used in fall detection studies, with an Area Under the Curve of 0.918. This result suggests that the proposed wavelet-based feature is promising and future studies could use this feature (in combination with others considering different fall phases) in order to improve the performance of fall detection algorithms.

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Multimodal sensor-based fall detection within the domestic environment of elderly people

Cited by 20 publications

References 23 publications

Could In-Home Sensors Surpass Human Observation of People with Parkinson’s at High Risk of Falling? An Ethnographic Study

Could In-Home Sensors Surpass Human Observation of People with Parkinson’s at High Risk of Falling? An Ethnographic Study

Fall Detection System for the Elderly Based on the Classification of Shimmer Sensor Prototype Data

A Wavelet-Based Approach to Fall Detection

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