The Smart Insole: A Pilot Study of Fall Detection

Qian, Xiaoye; Cheng, Haoyou; Chen, Diliang; Liu, Quan; Chen, Huan; Jiang, Haotian; Huang, Ming-Chun

doi:10.1007/978-3-030-34833-5_4

Cited by 12 publications

(5 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Autoencoders are usually adopted to extract feature representations [22] and handling missing data issues [23], [24]. Convolutional neural networks (CNNs) are suited for processing grid data in capturing local dynamic characteristics [25] or processing signals in the frequency domain [26]- [28]. Recurrent neural networks (RNNs) and their variants LSTMs and GRUs based soft sensors were developed to estimate variables with strong temporal patterns [29], and to cope with strong nonlinearity and dynamics of the process [30].…”

Section: A Deep Learning In Soft Sensingmentioning

confidence: 99%

GraSSNet: Graph Soft Sensing Neural Networks

Huang¹,

Zhang²,

Yella³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

In the era of big data, data-driven based classification has become an essential method in smart manufacturing to guide production and optimize inspection. The industrial data obtained in practice is usually time-series data collected by soft sensors, which are highly nonlinear, nonstationary, imbalanced, and noisy. Most existing soft-sensing machine learning models focus on capturing either intra-series temporal dependencies or pre-defined inter-series correlations, while ignoring the correlation between labels as each instance is associated with multiple labels simultaneously. In this paper, we propose a novel graph based soft-sensing neural network (GraSSNet) for multivariate time-series classification of noisy and highly-imbalanced softsensing data. The proposed GraSSNet is able to 1) capture the inter-series and intra-series dependencies jointly in the spectral domain; 2) exploit the label correlations by superimposing label graph that built from statistical co-occurrence information; 3) learn features with attention mechanism from both textual and numerical domain; and 4) leverage unlabeled data and mitigate data imbalance by semi-supervised learning. Comparative studies with other commonly used classifiers are carried out on Seagate soft sensing data, and the experimental results validate the competitive performance of our proposed method.

show abstract

Section: A Deep Learning In Soft Sensingmentioning

confidence: 99%

GraSSNet: Graph Soft Sensing Neural Networks

Huang¹,

Zhang²,

Yella³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Deep visualization is proposed to build the trust in the model by evaluating its output. Deep visualization can be regarded as an efficient way to open and explain the "black-box" of DNNs [31]. The deep visualization has been successfully applied to computer vision and natural language processing (NLP), but rarely utilized in soft-sensing system.…”

Section: Deep Visualizationmentioning

confidence: 99%

Soft Sensing Model Visualization: Fine-tuning Neural Network from What Model Learned

Qian¹,

Zhang²,

Yella³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

The growing availability of the data collected from smart manufacturing is changing the paradigms of production monitoring and control. The increasing complexity and content of the wafer manufacturing process in addition to the timevarying unexpected disturbances and uncertainties, make it infeasible to do the control process with model-based approaches. As a result, data-driven soft-sensing modeling has become more prevalent in wafer process diagnostics. Recently, deep learning has been utilized in soft sensing system with promising performance on highly nonlinear and dynamic time-series data. Despite its successes in soft-sensing systems, however, the underlying logic of the deep learning framework is hard to understand. In this paper, we propose a deep learning-based model for defective wafer detection using a highly imbalanced dataset. To understand how the proposed model works, the deep visualization approach is applied. Additionally, the model is then fine-tuned guided by the deep visualization. Extensive experiments are performed to validate the effectiveness of the proposed system. The results provide an interpretation of how the model works and an instructive fine-tuning method based on the interpretation.

show abstract

“…Hsu et al built up a wearable system including inertial sensing modules to recognize sports activities [12]. Qian et al proposed a smart insole based on pressure sensors for fall detection [13]. Yang et al designed a wearable system based on air pressure and inertial measurement unit sensors to improve the performance of human activity recognition (HAR) [14].…”

Section: Introductionmentioning

confidence: 99%

Activity Recognition Using Different Sensor Modalities and Deep Learning

Ascioglu,

Senol

2023

Applied Sciences

View full text Add to dashboard Cite

In recent years, human activity monitoring and recognition have gained importance in providing valuable information to improve the quality of life. A lack of activity can cause health problems including falling, depression, and decreased mobility. Continuous activity monitoring can be useful to prevent progressive health problems. With this purpose, this study presents a wireless smart insole with four force-sensitive resistors (FSRs) that monitor foot contact states during activities for both indoor and outdoor use. The designed insole is a compact solution and provides walking comfort with a slim and flexible structure. Moreover, the inertial measurement unit (IMU) sensors designed in our previous study were used to collect 3-axis accelerometer and 3-axis gyroscope outputs. Smart insoles were located in the shoe sole for both right and left feet, and two IMU sensors were attached to the thigh area of each leg. The sensor outputs were collected and recorded from forty healthy volunteers for eight different gait-based activities including walking uphill and descending stairs. The obtained datasets were separated into three categories; foot contact states, the combination of acceleration and gyroscope outputs, and a set of all sensor outputs. The dataset for each category was separately fed into deep learning algorithms, namely, convolutional long–short-term memory neural networks. The performance of each neural network for each category type was examined. The results show that the neural network using only foot contact states presents 90.1% accuracy and provides better performance than the combination of acceleration and gyroscope datasets for activity recognition. Moreover, the neural network presents the best results with 93.4% accuracy using a combination of all the data compared with the other two categories.

show abstract

The Smart Insole: A Pilot Study of Fall Detection

Cited by 12 publications

References 15 publications

GraSSNet: Graph Soft Sensing Neural Networks

GraSSNet: Graph Soft Sensing Neural Networks

Soft Sensing Model Visualization: Fine-tuning Neural Network from What Model Learned

Activity Recognition Using Different Sensor Modalities and Deep Learning

Contact Info

Product

Resources

About