Wearable Band Using a Fabric-Based Sensor for Exercise ECG Monitoring

Shen, Chien-Lung; Kao, Tsair; Huang, Ching‐Tang; Lee, Jun-huei

doi:10.1109/iswc.2006.286367

Cited by 20 publications

(11 citation statements)

References 4 publications

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“…In that study various types of ECG devices with different electrode positions were used. The studies by Shen et al [27], and by Valchinov et al [28] reported that the ECG signal was of adequate quality during resting, walking, or jogging, but there were some small motion artefacts present during running or jumping activities. In our preliminary pilot study [18] it was shown that the particular wireless ECG body sensor can correctly detect ECG signal on a cycle ergometer; however, on the treadmill the test signal was of adequate quality for running speeds of up to 13.5 km/h, while in the presented study it was of adequate quality up to 14.96 km/h.…”

Section: Discussionmentioning

confidence: 99%

Quality of One-channel Telemetric ECG Sensor Signal in Maximum Exercise Stress Tests

Siraiy

Trobec

Ilić

2019

Measurement Science Review

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The aim of this study was to evaluate the quality of the ECG signal, obtained from a telemetric body-sensor device during a maximum stress test on an ergometer. Twenty-three subjects, 13 males, were included in the study (20.56±1.19 years). Two different sensor positions were verified on each subject by the concurrent use of two ECG sensors. Each subject participated in four exercise stress tests: two on a treadmill and two on a cycle ergometer. In the first test, both sensors were attached to self-adhesive skin electrodes on the body, while in the second test the sensors were additionally fixed with self-adhesive tapes. The measurements were compared on both ergometers, in terms of the ECG sensors’ positions and the methods used for the sensors’ fixation. The results showed a significant difference in the running speed that provides an assessable ECG signal between the non-fixed and the fixed sensors at position left inferior (p = 0.000), as well as between the positions left inferior and left superior in the first (p = 0.019), and in the second test (p = 0.000) on the treadmill. On the cycle ergometer the differences were significant between the positions left inferior and left superior in the first (p = 0.000), and the second test (p = 0.003), and between the tests with fixed and non-fixed sensors in the position left superior (p = 0.011). The study confirms that ECG sensors could be used for maximal exercise stress tests in laboratories, especially on a cycle ergometer, and that they present a great potential for future use of ECG sensors during physical activity.

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Section: Discussionmentioning

confidence: 99%

Quality of One-channel Telemetric ECG Sensor Signal in Maximum Exercise Stress Tests

Siraiy

Trobec

Ilić

2019

Measurement Science Review

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“…With the increasing spread of smartphones and wearable devices equipped with various sensors, human activities, biometric information, and surrounding situations can be recognized anytime and anywhere through sensor data, e.g., such as acceleration [11], angular velocity, light, pulse, position, radio wave status, electromyogram [21], electrocardiogram [7], galvanic skin reflexes [15], and manually configured devices [17]. The obtained information is applied to many services, e.g., a health management system [15] that automatically extracts life patterns and warns of lack of exercise and overwork, support during assembly and maintenance tasks [31] that presents manuals and required tools by predicting the next task from the current operation, medical support systems that record time of medication and blood sugar level measurement outside hospital environments, sports support systems to acquire the number of times of tackle and sprint and strength [6], entertainment whose effect changes according to audience behavior [25], personal authentication based on gait, input interfaces, and games.…”

Section: Introductionmentioning

confidence: 99%

Annotation Method for Human Activity and Device State Recognition Based on Smartphone Notification Removals

Sawano

Murao

2020

Journal of Information Processing

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With the increasing spread of smartphones and wearable devices equipped with various sensors, human activities, biometric information, and surrounding situations can be recognized. The process of human activity recognition must construct a model that has learned annotated sensor data, i.e., ground truth, labels, or answer activity, in advance. Therefore, a large and diverse set of annotated data is required to improve and evaluate model performance. It is difficult to judge a user's situation even after observing acceleration data; thus, it is necessary to annotate the collected acceleration data. In this paper, we propose a method to estimate user and device situations from the user's response to a notification generated by a device, e.g., a smartphone. The user and device situations are estimated from the user's response time to the notification and the device's acceleration values. An estimation result with high confidence is given to the sensor data as an annotation. Increasing the frequency of notifications, response to the notifications can be used as a sensor. We assume that acceleration values are affected by a user and device situation when the device notifications are taken instantly after its generation. The system pursues a high precision of estimation by selecting input acceleration data based on the interaction to the notification so that the estimations can be used as annotations. Through an evaluation experiment, for seven types of annotation classes, an average precision of 0.769 and 0.963 for user-independent experiments and user-dependent experiments were achieved, respectively. We also tested the proposed method in a natural environment, where 25 correct annotations were given for 45 responses to notifications, no annotations were given for 19 responses, and only one incorrect notification was observed.

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“…Along with the progress in wearable computing, many activity recognition systems with various kinds of sensors have recently been introduced, such as systems with electromyographs [16], electrocardiograms [17], Galvnanic skin response (GSR) [18], and manually configured devices [19]. Activity recognition systems are applied to many services i.e., health care [18], recognition of workers' routine activities [5], and support during assembly and maintenance tasks [20].…”

Section: Introductionmentioning

confidence: 99%

A Combined-activity Recognition Method with Accelerometers

Murao

Terada

2016

Journal of Information Processing

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Many activity recognition systems using accelerometers have been proposed. Activities that have been recognized are single activities which can be expressed with one verb, such as sitting, walking, holding a mobile phone, and throwing a ball. In fact, combined activities that include more than two kinds of state and movement are often taking place. Focusing on hand gestures, they are performed not only while standing, but also while walking and sitting. Though the simplest way to recognize such combined activities is to construct the recognition models for all the possible combinations of the activities, the number of combinations becomes immense. In this paper, firstly we propose a method that classifies activities into postures (e.g., sitting), behaviors (e.g., walking), and gestures (e.g., a punch) by using the autocorrelation of the acceleration values. Postures and behaviors are states lasting for a certain length of time. Gestures, however, are sporadic or once-off actions. It has been a challenging task to find gestures buried in other activities. Then, by utilizing the technique, we propose a recognition method for combined activities by learning single activities only. Evaluation results confirmed that our proposed method achieved 0.84 recall and 0.86 precision, which is comparable to the method that had learned all the combined activities.

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Wearable Band Using a Fabric-Based Sensor for Exercise ECG Monitoring

Cited by 20 publications

References 4 publications

Quality of One-channel Telemetric ECG Sensor Signal in Maximum Exercise Stress Tests

Quality of One-channel Telemetric ECG Sensor Signal in Maximum Exercise Stress Tests

Annotation Method for Human Activity and Device State Recognition Based on Smartphone Notification Removals

A Combined-activity Recognition Method with Accelerometers

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