Wearable System for Daily Activity Recognition Using Inertial and Pressure Sensors of a Smart Band and Smart Shoes

Truong, Phuc Huu; You, Sujeong; Ji, Sang-Hoon; Jeong, Gu-Min

doi:10.15837/ijccc.2019.6.3618

Cited by 15 publications

(16 citation statements)

References 35 publications

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“…Wearable smart materials can be attached directly to any part of the body, and also they can be accessories, such as watches, glasses, necklaces, and bracelets to monitor continuous parameters released from body. As an example, smart t-shirts and shoes are equipped with sensors to collect data and health information since individuals can easily carry them throughout the day [ 193 ]. The information collected from these devices include body temperature, heart rate, and respiratory rate.…”

Section: Smart Materials For Sensor Technologiesmentioning

confidence: 99%

Smart materials-integrated sensor technologies for COVID-19 diagnosis

et al. 2021

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After the first case has appeared in China, the COVID-19 pandemic continues to pose an omnipresent threat to global health, affecting more than 70 million patients and leading to around 1.6 million deaths. To implement rapid and effective clinical management, early diagnosis is the mainstay. Today, real-time reverse transcriptase (RT)-PCR test is the major diagnostic practice as a gold standard method for accurate diagnosis of this disease. On the other side, serological assays are easy to be implemented for the disease screening. Considering the limitations of today's tests including lengthy assay time, cost, the need for skilled personnel, and specialized infrastructure, both strategies, however, have impediments to be applied to the resource-scarce settings. Therefore, there is an urgent need to democratize all these practices to be applicable across the globe, specifically to the locations comprising of very limited infrastructure. In this regard, sensor systems have been utilized in clinical diagnostics largely, holding great potential to have pivotal roles as an alternative or complementary options to these current tests, providing crucial fashions such as being suitable for point-of-care settings, cost-effective, and having short turnover time. In particular, the integration of smart materials into sensor technologies leverages their analytical performances, including sensitivity, linear dynamic range, and specificity. Herein, we comprehensively review major smart materials such as nanomaterials, photosensitive materials, electrically sensitive materials, their integration with sensor platforms, and applications as wearable tools within the scope of the COVID-19 diagnosis.

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Section: Smart Materials For Sensor Technologiesmentioning

confidence: 99%

Smart materials-integrated sensor technologies for COVID-19 diagnosis

et al. 2021

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“…Figure 1 shows the structure of the insole sensor module. Recently, various studies using smart shoes have been conducted [17][18][19][20][21][22][23]. Three types of walking activities were classified in [17] using smart-shoes data.…”

Section: Related Work 21 Smart Shoesmentioning

confidence: 99%

“…In order to recognize human behavior using wearable devices, many studies have been made using smart bands [10,11], smartphones [12][13][14][15][16], and smart shoes [17][18][19][20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

“…However, there were limitations in using human direct walking information. Hence, smart shoes are becoming essential sensors for distinguishing user behaviors [17][18][19][20][21][22][23][24][25][26]. Since smart shoes can reflect human walking information touching the ground directly, we can gather relatively useful information to analyze user behaviors.…”

Section: Introductionmentioning

confidence: 99%

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Stack LSTM-Based User Identification Using Smart Shoes with Accelerometer Data

Kim

Lee

Jeong

2021

Sensors

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In this study, we propose a long short-term memory (LSTM)-based user identification method using accelerometer data from smart shoes. In general, for the user identification with human walking data, we require a pre-processing stage in order to divide human walking data into individual steps. Next, user identification can be made with divided step data. In these approaches, when there exist partial data that cannot complete a single step, it is difficult to apply those data to the classification. Considering these facts, in this study, we present a stack LSTM-based user identification method for smart-shoes data. Rather than using a complicated analysis method, we designed an LSTM network for user identification with accelerometer data of smart shoes. In order to learn partial data, the LSTM network was trained using walking data with random sizes and random locations. Then, the identification can be made without any additional analysis such as step division. In the experiments, user walking data with 10 m were used. The experimental results show that the average recognition rate was about 93.41%, 97.19%, and 98.26% by using walking data of 2.6, 3.9, and 5.2 s, respectively. With the experimental results, we show that the proposed method can classify users effectively.

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“…Human activity recognition (HAR), as a new human–computer interaction technology, has become one of the research hotspots in the field of IoT. The existing HAR technology can be divided into three types according to the implementation methods, namely, camera-based [ 1 , 2 ], sensor-based [ 3 , 4 ] and wireless signal-based [ 5 ]. The wireless signal-based recognition method has attracted more and more attention for its advantages of not being affected by indoor light, not requiring users to wear additional equipment and protecting privacy.…”

Section: Introductionmentioning

confidence: 99%

Low-Cost and Device-Free Human Activity Recognition Based on Hierarchical Learning Model

Chen

Huang

Jiang

et al. 2021

Sensors

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Human activity recognition (HAR) has been a vital human–computer interaction service in smart homes. It is still a challenging task due to the diversity and similarity of human actions. In this paper, a novel hierarchical deep learning-based methodology equipped with low-cost sensors is proposed for high-accuracy device-free human activity recognition. ESP8266, as the sensing hardware, was utilized to deploy the WiFi sensor network and collect multi-dimensional received signal strength indicator (RSSI) records. The proposed learning model presents a coarse-to-fine hierarchical classification framework with two-level perception modules. In the coarse-level stage, twelve statistical features of time–frequency domains were extracted from the RSSI measurements filtered by a butterworth low-pass filter, and a support vector machine (SVM) model was employed to quickly recognize the basic human activities by classifying the signal statistical features. In the fine-level stage, the gated recurrent unit (GRU), a representative type of recurrent neural network (RNN), was applied to address issues of the confused recognition of similar activities. The GRU model can realize automatic multi-level feature extraction from the RSSI measurements and accurately discriminate the similar activities. The experimental results show that the proposed approach achieved recognition accuracies of 96.45% and 94.59% for six types of activities in two different environments and performed better compared the traditional pattern-based methods. The proposed hierarchical learning method provides a low-cost sensor-based HAR framework to enhance the recognition accuracy and modeling efficiency.

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Wearable System for Daily Activity Recognition Using Inertial and Pressure Sensors of a Smart Band and Smart Shoes

Cited by 15 publications

References 35 publications

Smart materials-integrated sensor technologies for COVID-19 diagnosis

Smart materials-integrated sensor technologies for COVID-19 diagnosis

Stack LSTM-Based User Identification Using Smart Shoes with Accelerometer Data

Low-Cost and Device-Free Human Activity Recognition Based on Hierarchical Learning Model

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