2023
DOI: 10.3390/s23010495
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Deep Neural Network for the Detections of Fall and Physical Activities Using Foot Pressures and Inertial Sensing

Abstract: Fall detection and physical activity (PA) classification are important health maintenance issues for the elderly and people with mobility dysfunctions. The literature review showed that most studies concerning fall detection and PA classification addressed these issues individually, and many were based on inertial sensing from the trunk and upper extremities. While shoes are common footwear in daily off-bed activities, most of the aforementioned studies did not focus much on shoe-based measurements. In this pa… Show more

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Cited by 11 publications
(7 citation statements)
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“…These FSRs were manufactured using processing and printing-based micromachining technology, employing a resistance-type piezo-resistive polymer composite. Each FSR had a sensing range of 1–5 kg/cm 2 and was individually calibrated using elastic-film pressurization to minimize resistance variance among the sensors ( Chan et al, 2023 ). The custom insole had dimensions of 260 mm in height, 85 mm in metatarsus width, 55 mm in heel width, and a thickness of 0.63 mm.…”
Section: Methodsmentioning
confidence: 99%
“…These FSRs were manufactured using processing and printing-based micromachining technology, employing a resistance-type piezo-resistive polymer composite. Each FSR had a sensing range of 1–5 kg/cm 2 and was individually calibrated using elastic-film pressurization to minimize resistance variance among the sensors ( Chan et al, 2023 ). The custom insole had dimensions of 260 mm in height, 85 mm in metatarsus width, 55 mm in heel width, and a thickness of 0.63 mm.…”
Section: Methodsmentioning
confidence: 99%
“…The future of research in the area of assistive technologies design lies in the increased use of robotic devices, personalised by 3D printing; Internet of Things wearable sensors for personalised training of limb use, gait and balance, and activities of daily living and health monitoring [17][18][19][20][21]; and even novel brain-computer interfaces [22,23] and associated computational models [24]. Further, more advanced studies may additionally use data from postural and gait analysis [25,26]. We will also draw inspiration as to the direction of further research from the work of [27][28][29].…”
Section: Directions For Further Researchmentioning
confidence: 99%
“…Shalin et al (2021) focused on the efficacy of gait freeze detection and prediction using plantar pressure sensors, validating the suitability of LSTM networks for this type of data. Chan (2023) explored fall detection using a hybrid CNN and RNN model, proving the model's applicability to both plantar pressure and inertial sensor data.…”
Section: Comparison With Contemporary Research Findingsmentioning
confidence: 99%
“…Their model exhibited an average sensitivity of 82.1% and specificity of 89.5%. Further advancing the field, Chan et al (2023) developed an intelligent shoe system integrating pressure and inertial sensors. They crafted a hybrid model combining convolutional neural network (CNN) and recurrent neural network (RNN) techniques, achieving a remarkable F1score of 99.8%.…”
Section: Introductionmentioning
confidence: 99%