Intelligent Fall Detection with Wearable IoT

Ahamed, Farhad; Shahrestani, Seyed; Cheung, Hon

doi:10.1007/978-3-030-22354-0_35

Cited by 6 publications

(6 citation statements)

References 10 publications

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“…Much of the research in the indirect activity recognition field is focused on fall detection [ 24 , 25 ]. Sadreazami et al [ 24 ] utilized Standoff Radar and a time series-based method to detect fall incidents.…”

Section: Related Workmentioning

confidence: 99%

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Human Activity Classification Using Multilayer Perceptron

Gorjani

Byrtus

Dohnal

et al. 2021

Sensors

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The number of smart homes is rapidly increasing. Smart homes typically feature functions such as voice-activated functions, automation, monitoring, and tracking events. Besides comfort and convenience, the integration of smart home functionality with data processing methods can provide valuable information about the well-being of the smart home residence. This study is aimed at taking the data analysis within smart homes beyond occupancy monitoring and fall detection. This work uses a multilayer perceptron neural network to recognize multiple human activities from wrist- and ankle-worn devices. The developed models show very high recognition accuracy across all activity classes. The cross-validation results indicate accuracy levels above 98% across all models, and scoring evaluation methods only resulted in an average accuracy reduction of 10%.

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Section: Related Workmentioning

confidence: 99%

“…Sadreazami et al [ 24 ] utilized Standoff Radar and a time series-based method to detect fall incidents. Ahamed et al [ 25 ] used accelerometer-based data and deep learning methods for fall detection. Other researchers took activity recognition further than fall detection by recognizing multiple human behaviors.…”

Section: Related Workmentioning

confidence: 99%

Human Activity Classification Using Multilayer Perceptron

Gorjani

Byrtus

Dohnal

et al. 2021

Sensors

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“…The model parameters are optimized by minimizing weighted cross-entropy loss functions, based on the update rule of the adaptive moment estimation (ADAM) solver [38]. The ADAM solver normally performs better with RNNs than the default stochastic gradient descent with momentum (SGDM) solver [39].…”

Section: ) Bilstmmentioning

confidence: 99%

Machine Learning-Based Automatic Detection of Central Sleep Apnea Events From a Pressure Sensitive Mat

Azimi

Bouchard

et al. 2020

IEEE Access

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Polysomnography (PSG) is the standard test for diagnosing sleep apnea. However, the approach is obtrusive, time-consuming, and with limited access for patients in need of sleep apnea diagnosis. In recent years, there have been many attempts to search for an alternative device or approach that avoids the limitations of PSG. Pressure-sensitive mats (PSM) have proven to be able to detect central sleep apneas (CSA) and be a potential alternative for PSG. In the current study, we combine advanced machine learning approaches with a practical unobtrusive home monitoring device (PSM) to detect CSA events from data collected nocturnally and unattended. Two deep learning methods are implemented for the automatic detection of CSA events: a temporal convolutional network (TCN) and a bidirectional long short-term memory (BiLSTM) network. The deep learning models are compared to a classical machine learning approach (linear support vector machine, SVM) and a simple threshold-based algorithm. Considering the characteristics of each method, we choose strategies, including resampling and weighted cost-functions, to optimize the methods and to perform CSA detection as anomaly detection in an imbalanced data set. We evaluate the performance of all models on a database containing 7 days of data from 9 elderly patients. From the resulting 63 days, data from 7 patients (49 days) are devoted to training for optimizing hyperparameters, and data from 2 patients (14 days) are devoted to testing. Experimental results indicate that the bestperforming model achieves an accuracy of 95.1% through training an BiLSTM network. Overall, the implemented deep learning methods achieve better performance than the conventional classification approach (SVM) and the simple threshold-based method, and show good potential for the use of PSM for practical unobtrusive monitoring of CSA. INDEX TERMS Biomedical measurement, data analysis, deep learning, machine learning, patient monitoring, pressure measurement, central sleep apnea detection.

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“…Wiljer et al [ 4 ] suggested improving health care by developing an artificial intelligence-enabled healthcare practice. Many of the works in the field of activity recognition are emphasizing fall detections [ 5 , 6 , 7 ]. Sadreazami et al [ 5 ] proposed using the Standoff Radar and a time series-based method for detecting fall incidents in human daily activities.…”

Section: Introductionmentioning

confidence: 99%

“…A time was obtained by summing all the range bins corresponding to the ultra-wideband radar return signals. Ahamed et al [ 6 ] investigated accelerometer-based fall detection, the Feed Forward Neural Network and Long Short-Term Memory based on deep learning networks, applied to detect falls. Dhiraj et al [ 7 ] proposed two vision-based solutions, one using convolutional neural networks in 3D-mode and another using a hybrid approach by combining convolutional neural networks and long short-term memory networks using 360-degree videos for human fall detection.…”

Section: Introductionmentioning

confidence: 99%

Indirect Recognition of Predefined Human Activities

Gorjani

Proto

Vaňuš

et al. 2020

Sensors

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The work investigates the application of artificial neural networks and logistic regression for the recognition of activities performed by room occupants. KNX (Konnex) standard-based devices were selected for smart home automation and data collection. The obtained data from these devices (Humidity, CO2, temperature) were used in combination with two wearable gadgets to classify specific activities performed by the room occupant. The obtained classifications can benefit the occupant by monitoring the wellbeing of elderly residents and providing optimal air quality and temperature by utilizing heating, ventilation, and air conditioning control. The obtained results yield accurate classification.

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Intelligent Fall Detection with Wearable IoT

Cited by 6 publications

References 10 publications

Human Activity Classification Using Multilayer Perceptron

Human Activity Classification Using Multilayer Perceptron

Machine Learning-Based Automatic Detection of Central Sleep Apnea Events From a Pressure Sensitive Mat

Indirect Recognition of Predefined Human Activities

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