Wearable Biomedical Monitoring System Using TextileNet

Toda, Masashi; Akita, Junichi; Shimamoto, Shigeru; Yanagihara, K.; Kunita, Mihoko; Iwata, Kazunori

doi:10.1109/iswc.2006.286355

Cited by 12 publications

(7 citation statements)

References 2 publications

(1 reference statement)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the design and commercialization of clothes and garment accessories for humans, biomedical system has nowadays a great importance [14], and several examples proved this trend. Szczęsna et al [15] developed a prototype of a waistband with textile electrodes for monitoring of cardiac activity.…”

Section: Related Workmentioning

confidence: 99%

Design of a Long Sleeve T-Shirt with ECG and EMG for Athletes and Rehabilitation Patients

Paiva

Catarino

Carvalho

et al. 2018

Innovation, Engineering and Entrepreneurship

View full text Add to dashboard Cite

Considering the importance of strength training in sports injury prevention and rehabilitation, an e-textile system that assists the athlete's training and rehabilitation was designed. This paper reports the construction of a prototype of a T-shirt with embedded textile electrodes to monitor cardiac and muscle activity. Signal tests were conducted and are presented. It is the authors' intention to study and integrate other textile sensors that may provide useful information to the user.

show abstract

Section: Related Workmentioning

confidence: 99%

Design of a Long Sleeve T-Shirt with ECG and EMG for Athletes and Rehabilitation Patients

Paiva

Catarino

Carvalho

et al. 2018

Innovation, Engineering and Entrepreneurship

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

Wearable Biomedical Monitoring System Using TextileNet

Abstract: We developed and tested a biomedical monitoring system using TextileNet, a

Cited by 12 publications

References 2 publications

Design of a Long Sleeve T-Shirt with ECG and EMG for Athletes and Rehabilitation Patients

Design of a Long Sleeve T-Shirt with ECG and EMG for Athletes and Rehabilitation Patients

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

A Combined-activity Recognition Method with Accelerometers

Contact Info

Product

Resources

About