Soft Fall Detection Using Machine Learning in Wearable Devices

Genoud, Dominique; Cuendet, Vincent; Torrent, Julien

doi:10.1109/aina.2016.124

Cited by 16 publications

(11 citation statements)

References 15 publications

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“…This instance-based classifier has been utilized as a decision algorithm in works such as [ 17 , 50 , 51 , 52 , 53 , 54 ]. The typical operation of k -NN is represented in Figure 6 , utilizes the training dataset in a very simple way: whenever a new activity has to be classified, k -NN searches for the k already classified samples that are closest to this new uncategorized data.…”

Section: Machine Learning Algorithms and Selection Of The Input Fementioning

confidence: 99%

“…The use of Decision Trees has also been considered by the related literature [ 50 , 54 , 56 , 57 , 58 ]. The basic operation of the algorithm is exemplified in Figure 7 , makes use of the training data to create a decision tree that will allow assigning a class to the unclassified mobility patterns.…”

Section: Machine Learning Algorithms and Selection Of The Input Fementioning

confidence: 99%

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Analysis of a Smartphone-Based Architecture with Multiple Mobility Sensors for Fall Detection with Supervised Learning

Santoyo-Ramón

Casilari

Cano-García

2018

Sensors

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This paper describes a wearable Fall Detection System (FDS) based on a body-area network consisting of four nodes provided with inertial sensors and Bluetooth wireless interfaces. The signals captured by the nodes are sent to a smartphone which simultaneously acts as another sensing point. In contrast to many FDSs proposed by the literature (which only consider a single sensor), the multisensory nature of the prototype is utilized to investigate the impact of the number and the positions of the sensors on the effectiveness of the production of the fall detection decision. In particular, the study assesses the capability of four popular machine learning algorithms to discriminate the dynamics of the Activities of Daily Living (ADLs) and falls generated by a set of experimental subjects, when the combined use of the sensors located on different parts of the body is considered. Prior to this, the election of the statistics that optimize the characterization of the acceleration signals and the efficacy of the FDS is also investigated. As another important methodological novelty in this field, the statistical significance of all the results (an aspect which is usually neglected by other works) is validated by an analysis of variance (ANOVA).

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Section: Machine Learning Algorithms and Selection Of The Input Fementioning

confidence: 99%

Section: Machine Learning Algorithms and Selection Of The Input Fementioning

confidence: 99%

Analysis of a Smartphone-Based Architecture with Multiple Mobility Sensors for Fall Detection with Supervised Learning

Santoyo-Ramón

Casilari

Cano-García

2018

Sensors

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“…Outra abordagem relevanteé utilizar dispositivos vestíveis dotados de acelerômetros e outros sensores capazes de detectar um movimento de queda, conforme observado em [5], [6], [7]. Os dispositivos vestíveis como smart-watch, bracelete, cinta, etc, são amplamente utilizados em atividades esportivas, e como os mesmos já possuem diversos sensores nativos, esta abordagem tem apresentado um crescimento significativo nosúltimos anos em virtude dos paradigmas de monitoração, comunicação e processamento da Internet das Coisas [8].…”

Section: Trabalhos Relacionadosunclassified

Fall Monitor: Uma Ferramenta em Dispositivos Móveis Para Detecção de Quedas de Pacientes Monitorados e Acionamento de Socorro

Torquato¹,

Ribeiro²,

Monteiro³

et al. 2018

Anais De XXXVI Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Resumo-Idosos, pacientes que sofreram AVCs, doenças neurológicas, articulares, cardiovasculares e outras doenças como distrofias musculares são exemplos de casos que podem resultar redução da mobilidade do indivíduo. Portanto, o home care e monitoramento remoto são maneiras efetivas de mitigar o risco de quedas e acidentes. Tendo esta motivação, foi desenvolvida uma ferramenta para smart-phones para auxiliar esse grupo de pessoas. A solução propostaé capaz de detectar a queda de um paciente monitorado, alertar o cuidador ou responsável pelo paciente, e viabilizar a chegada de ajuda e serviços de emergência de forma eficaz por meio do envio da localização do paciente monitorado. Palavras-Chave-Detecção de queda, Smart-phone, Android. Abstract-Elderly, patients who suffered from strokes, neurological, articular, cardiovascular illness and other diseases like muscular dystrophies are examples of cases which affect the mobility of individual. Remote monitoring and home care is an effective way to avoid the worsening of these sequels. With this question in mind, the development of a tool for smart-phones to assist these people, capable of detecting a fall of monitored patient was developed, which alerts the caregiver and makes easier the arrival of help to rescue the patient in an effective manner.

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“…The aim of most fall detection systems is not only to detect a fall but also to inform concerned authorities in case of an urgent medical emergency. Most of the latest algorithms for fall detection use machine learning [6] and deep learning algorithms [7].…”

Section: Introductionmentioning

confidence: 99%

Fall Detection from Electrocardiogram (ECG) Signals and Classification by Deep Transfer Learning

et al. 2021

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Fall is a prominent issue due to its severe consequences both physically and mentally. Fall detection and prevention is a critical area of research because it can help elderly people to depend less on caregivers and allow them to live and move more independently. Using electrocardiograms (ECG) signals independently for fall detection and activity classification is a novel approach used in this paper. An algorithm has been proposed which uses pre-trained convolutional neural networks AlexNet and GoogLeNet as a classifier between the fall and no fall scenarios using electrocardiogram signals. The ECGs for both falling and no falling cases were obtained as part of the study using eight volunteers. The signals are pre-processed using an elliptical filter for signal noises such as baseline wander and power-line interface. As feature extractors, frequency-time representations (scalograms) were obtained by applying a continuous wavelet transform on the filtered ECG signals. These scalograms were used as inputs to the neural network and a significant validation accuracy of 98.08% was achieved in the first model. The trained model is able to distinguish ECGs with a fall activity from an ECG with a no fall activity with an accuracy of 98.02%. For the verification of the robustness of the proposed algorithm, our experimental dataset was augmented by adding two different publicly available datasets to it. The second model can classify fall, daily activities and no activities with an accuracy of 98.44%. These models were developed by transfer learning from the domain of real images to the medical images. In comparison to traditional deep learning approaches, the transfer learning not only avoids “reinventing the wheel,” but also presents a lightweight solution to otherwise computationally heavy problems.

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Soft Fall Detection Using Machine Learning in Wearable Devices

Cited by 16 publications

References 15 publications

Analysis of a Smartphone-Based Architecture with Multiple Mobility Sensors for Fall Detection with Supervised Learning

Analysis of a Smartphone-Based Architecture with Multiple Mobility Sensors for Fall Detection with Supervised Learning

Fall Monitor: Uma Ferramenta em Dispositivos Móveis Para Detecção de Quedas de Pacientes Monitorados e Acionamento de Socorro

Fall Detection from Electrocardiogram (ECG) Signals and Classification by Deep Transfer Learning

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