Killer heuristic optimized convolution neural network-based fall detection with wearable IoT sensor devices

Alarifi, Abdulaziz; Alwadain, Ayed

doi:10.1016/j.measurement.2020.108258

Cited by 65 publications

(37 citation statements)

References 33 publications

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“…The system was trained using SVM and had a performance accuracy of 96.33%. A wearable sensor driven fall detection system was proposed by Alarifi [50]. The wearables were placed on six different locations on the user's body to track multimodal components of motion and behavior data, which were studied and analyzed by a convolution neural network.…”

Section: Literature Reviewmentioning

confidence: 99%

Multimodal Approaches for Indoor Localization for Ambient Assisted Living in Smart Homes

Thakur

Han

2021

Information

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This work makes multiple scientific contributions to the field of Indoor Localization for Ambient Assisted Living in Smart Homes. First, it presents a Big-Data driven methodology that studies the multimodal components of user interactions and analyzes the data from Bluetooth Low Energy (BLE) beacons and BLE scanners to detect a user’s indoor location in a specific ‘activity-based zone’ during Activities of Daily Living. Second, it introduces a context independent approach that can interpret the accelerometer and gyroscope data from diverse behavioral patterns to detect the ‘zone-based’ indoor location of a user in any Internet of Things (IoT)-based environment. These two approaches achieved performance accuracies of 81.36% and 81.13%, respectively, when tested on a dataset. Third, it presents a methodology to detect the spatial coordinates of a user’s indoor position that outperforms all similar works in this field, as per the associated root mean squared error—one of the performance evaluation metrics in ISO/IEC18305:2016—an international standard for testing Localization and Tracking Systems. Finally, it presents a comprehensive comparative study that includes Random Forest, Artificial Neural Network, Decision Tree, Support Vector Machine, k-NN, Gradient Boosted Trees, Deep Learning, and Linear Regression, to address the challenge of identifying the optimal machine learning approach for Indoor Localization.

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Section: Literature Reviewmentioning

confidence: 99%

Multimodal Approaches for Indoor Localization for Ambient Assisted Living in Smart Homes

Thakur

Han

2021

Information

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“…Non-linear SVM 49 A fall detection and prediction mechanism was designed with an non-linear SVM to identify the falls and to alert them before a fall occurs.…”

Section: Observations Limitationsmentioning

confidence: 99%

“…With KNN classifier the approach obtained the highest accuracy of 99.80% and with RF classifier the approach obtained 96.82% to recognize the falling activities. Saadeh et al 49 presented an IoT‐based wearable FDS and fall prediction model. The fall prediction is designed in a fast mode way and predictions before a fall is designed in a slow mode.…”

Section: Review On Ml‐based Fall Detection Techniquementioning

confidence: 99%

A systematic review on machine learning for fall detection system

Rastogi

Singh

2021

Computational Intelligence

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Fall is a major threat to the health and life of the elders. A Fall Detection System (FDS) assist the elders by identifying the fall and save their life. Machine Learning-(ML) based FDS has turned into a major research area due to its capability to assist the elders automatically. The efficiency of a FDS depends on its strength to identify the fall from nonfall accurately. The initial fall detection scheme depends on the threshold-based classification to classify the fall from the Activity of Daily Living (ADL) but this classification method has failed to reduce the false alarm rate, which raises a question on the efficiency of the technique. This review work identifies the problems in threshold-based classification from existing works and finds the need for an efficient ML-based classification technique to accurately identify the fall. Then, presents a comprehensive literature review on various ML-based classification in fall detection. Moreover, the scrutiny investigates the shortcomings associated with the ML-based techniques for future research. This study finds that present ML-based FDS has not addressed problems like data preprocessing and data dimensionality reduction techniques even though ML-based techniques are far superior to threshold-based techniques.The study concludes that Self-Adaptive-based FDS, as

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“…Another typical method for fall detection is to use a system with a motion sensor such as an inertial measurement unit (IMU). Using parameters such as velocity, angular velocity, direction, and acceleration recorded by the IMU, the IMU can control instruments such as robots, track human joint information, classify behavior or detect falls [30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47].…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Network–Based Double-Check Method for Fall Detection Using IMU-L Sensor and RGB Camera Data

et al. 2021

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Existing methods for fall detection may not detect a fall when it occurs or may generate a false alarm when a fall does not occur. In order to overcome these limitations and detect falls with 100% accuracy, a double-check method for fall detection in elderly people via an inertial measurement unit-location (IMU-L) sensor and a red-green-blue (RGB) camera is proposed. The IMU-L sensor is a combination of an IMU sensor (accelerometer and gyroscope) and an ultrawideband signal-based location sensor; the RGB sensor is mounted on a robot. The proposed method involves detecting and confirming the fall of an elderly individual via the IMU-L sensor and an RGB image, respectively. The IMU-L sensor is worn on the body to detect falls. When a potential fall occurs, the individual's location information is synchronized with the motion data. During detection, because of the sequential nature of IMU data, a deep learning technique called a recurrent neural network (RNN) is trained to classify falls. When the IMU indicates a suspected fall situation, the robot moves to the corresponding location and confirms whether a fall has occurred. During the confirmation stage, a convolutional neural network-based technique is applied to the RGB image data to recognize and confirm the fall. Repeated confirmed fall detections using this method classified falls more accurately than existing methods that use only an IMU sensor. We conducted a real-time experiment to validate our method using a dataset developed in a laboratory and achieved 100% accuracy in our experimental environment. INDEX TERMS convolutional neural network, deep learning, elderly fall, fall detection, motion data with location, recurrent neural network, transfer learning.

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Killer heuristic optimized convolution neural network-based fall detection with wearable IoT sensor devices

Cited by 65 publications

References 33 publications

Multimodal Approaches for Indoor Localization for Ambient Assisted Living in Smart Homes

Multimodal Approaches for Indoor Localization for Ambient Assisted Living in Smart Homes

A systematic review on machine learning for fall detection system

Deep Neural Network–Based Double-Check Method for Fall Detection Using IMU-L Sensor and RGB Camera Data

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