Window-size impact on detection rate of wearable-sensor-based fall detection using supervised machine learning

Putra, I Putu Edy Suardiyana; Vesilo, Rein

doi:10.1109/lsc.2017.8268134

Cited by 16 publications

(10 citation statements)

References 21 publications

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“…Support Vector Machine (SVM) is perhaps the most popular learning algorithm employed in the fall detection system literature [ 31 , 47 , 48 , 49 , 50 , 51 ]. According to the SVM algorithm, the input space defined trough the features of the different training samples is converted into a multi-dimensional space by means of a non-linear mapping.…”

Section: Machine Learning Algorithms and Selection Of The Input Fementioning

confidence: 99%

“…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%

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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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“…This is because the SisFall dataset has a uniform length (15 s) for its fall data. This length can predictably give a better F-score for the classifier (see [ 39 ] for an analysis of window size on the SisFall dataset). However, the length of human activities is unpredictable in real cases.…”

Section: Parameter Selection For Event-mlmentioning

confidence: 99%

An Event-Triggered Machine Learning Approach for Accelerometer-Based Fall Detection

Putra

Brusey

Gaura

et al. 2017

Sensors

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The fixed-size non-overlapping sliding window (FNSW) and fixed-size overlapping sliding window (FOSW) approaches are the most commonly used data-segmentation techniques in machine learning-based fall detection using accelerometer sensors. However, these techniques do not segment by fall stages (pre-impact, impact, and post-impact) and thus useful information is lost, which may reduce the detection rate of the classifier. Aligning the segment with the fall stage is difficult, as the segment size varies. We propose an event-triggered machine learning (EvenT-ML) approach that aligns each fall stage so that the characteristic features of the fall stages are more easily recognized. To evaluate our approach, two publicly accessible datasets were used. Classification and regression tree (CART), k-nearest neighbor (k-NN), logistic regression (LR), and the support vector machine (SVM) were used to train the classifiers. EvenT-ML gives classifier F-scores of 98% for a chest-worn sensor and 92% for a waist-worn sensor, and significantly reduces the computational cost compared with the FNSW- and FOSW-based approaches, with reductions of up to 8-fold and 78-fold, respectively. EvenT-ML achieves a significantly better F-score than existing fall detection approaches. These results indicate that aligning feature segments with fall stages significantly increases the detection rate and reduces the computational cost.

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“…To improve the accuracy on the prediction of gait cycle, an SWD prediction scheme was proposed. A SWD is normally used to segment a data sequence [30], which in our study SWD on the input parameter of hip angle, knee angle and ground reaction force is used. This study used three different window size of 3 gait cycles for training and validation of the gait cycle prediction models.…”

Section: Sliding Window Data (Swd)mentioning

confidence: 99%

Gait cycle prediction model based on gait kinematic using machine learning technique for assistive rehabilitation device

Izhar

Hussain

Maruzuki

et al. 2021

IJ-AI

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The gait cycle prediction model is critical for controlling assistive rehabilitation equipment like orthosis. The human gait model has recently used statistical models, but the dynamic properties of human physiology limit the current approach. Current human gait cycle prediction models need detailed kinematic and kinetic data of the human body as input parameters, and measuring them requires special instruments, making them difficult to use in real-world applications. In our study, three separate machine learning algorithms were used to create a human gait model: Gaussian process regression, support vector machine, and decision tree. The algorithm used to create the model's input parameters are height, weight, hip and knee angle, and ground reaction force (GRF). For better gait cycle model prediction, the models produced were enhanced by incorporating different sliding window data. The best gait period prediction model was DT with sliding window data (t−3), which had a root mean square error of 3.3018 and the R-squared (R-Value) of 0.97. The projection model focused on hip and knee angle and GRF was a feasible solution to controlling assistive rehabilitation devices during the gait cycle.

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Window-size impact on detection rate of wearable-sensor-based fall detection using supervised machine learning

Cited by 16 publications

References 21 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

An Event-Triggered Machine Learning Approach for Accelerometer-Based Fall Detection

Gait cycle prediction model based on gait kinematic using machine learning technique for assistive rehabilitation device

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