Design and Evaluation of a Wearable Optical Sensor for Monitoring Seated Spinal Posture

Dunne, Lucy E.; Walsh, Pauline; Smyth, Barry; Caulfield, Brian

doi:10.1109/iswc.2006.286345

Cited by 70 publications

(41 citation statements)

References 9 publications

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“…In [8] a glove was realized using piezoelectric film sensors. Fiberoptics sensors were used in a pressure suit [2] and to measure the seated spinal posture [4]. A commercially available system measuring the posture of the spine is based on ultra-1-4244-1453-9/07/$25.00 ©2007 IEEE.…”

Section: Related Workmentioning

confidence: 99%

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Recognizing Upper Body Postures using Textile Strain Sensors

Mattmann

Amft

Harms

et al. 2007

2007 11th IEEE International Symposium on Wearable Computers

171

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In this paper we present a garment prototype using strain sensors to recognize upper body postures. A novel thermoplastic elastomer strain sensor was used for measuring strain in the clothing. This sensor has a linear resistance response to strain, a small hysteresis and can be fully integrated into textile.A study was conducted with eight participants wearing the garment and performing a total of 27 upper body postures. A Naïve Bayes classification was applied to identify the different postures. Nearly a complete recognition rate of 97% was achieved when the classification was adapted to the individual participant. A classification rate of 84% was achieved for an all-user classification and 65% for an independent user. These results show the feasibility to recognize postures with our setup, even in an unseen user setting.Furthermore, we used the garment prototype in a gym experiment to explore its potential for rehabilitation and fitness training. Intensity, speed and number of repetitions could be obtained from the garment sensor data.

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Section: Related Workmentioning

confidence: 99%

“…Farringdon et al concentrate in [5] on the textile integration of their knit strain sensor but do not give any measurement results. The fiberoptic approach of Dunne et al [4] focuses on measuring the seated spinal posture and is limited to bending back postures (one degree of freedom).…”

Section: Related Workmentioning

confidence: 99%

Recognizing Upper Body Postures using Textile Strain Sensors

Mattmann

Amft

Harms

et al. 2007

2007 11th IEEE International Symposium on Wearable Computers

171

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show abstract

“…For instance, body positioning monitoring, for example, accurateness of action performing in athletic exercise to avoid injury and target selected muscle groups [84,86]. Snug clothes are usually close to the body; therefore they could be used to monitor posture [87,88], perform an Electromyography (EMG) technique, and detecting fatigue for vehicular drivers [89]. The second principal benefit is that E-textile permits modification of clothing, therefore smart textile can act as camouflage, or even heal wounded soldiers [90].…”

Section: E-textilementioning

confidence: 99%

Healthcare in the Smart Home: A Study of Past, Present and Future

2017

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Ubiquitous or Pervasive Computing is an increasingly used term throughout the technology industry and is beginning to enter the consumer electronics space in its most recent form under the umbrella term: "Internet of Things". One area of focus is in augmenting the home with intelligent, networked sensors and computers to create a Smart Home which opens a host of possibilities for the role of tomorrow's dwelling. As the world's population continues to live longer and consequently experience more medical-related ailments, at the same time institutional healthcare is struggling to cope, the role of the Smart Home becomes paramount to monitoring a dweller's health and providing any necessary intervention. This study looks at the history of Smart Home Healthcare, current research areas, and potential areas of future investigation. Unique categorisations are presented in Activities of Daily Living (ADL) and Personal Sensors, and a thorough look at the application of Smart Home Healthcare is presented. Technology can augment traditional methods of healthcare delivery and in some cases completely replace it. Costs can be reduced and medical adherence can be increased, all of which contribute to a more sustainable and effective model of care.

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“…In healthcare applications, wearable sensors can be categorised based on the measured entity into subtypes that include biopotential sensors [1,2], optical sensors [3,4], stretch and pressure sensors [5,6], chemical sensors [7,8], and Inertial Measurement Units (IMUs) [9,10,11,12]. Commercialisation of devices using the different subtypes of sensors has been gradually increasing.…”

Section: Introductionmentioning

confidence: 99%

Activity Recognition Using Complex Network Analysis

Jalloul¹,

Porée²,

Viardot³

et al. 2018

IEEE J. Biomed. Health Inform.

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In this paper, we perform complex network analysis on a connectivity dataset retrieved from a monitoring system in order to classify simple daily activities. The monitoring system is composed of a set of wearable sensing modules positioned on the subject's body and the connectivity data consists of the correlation between each pair of modules. A number of network measures are then computed followed by the application of statistical significance and feature selection methods. These methods were implemented for the purpose of reducing the total number of modules in the monitoring system required to provide accurate activity classification. The obtained results show that an overall accuracy of 84.6% for activity classification is achieved, using a Random Forest (RF) classifier, and when considering a monitoring system composed of only two modules positioned at the Neck and Thigh of the subject's body.

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Design and Evaluation of a Wearable Optical Sensor for Monitoring Seated Spinal Posture

Cited by 70 publications

References 9 publications

Recognizing Upper Body Postures using Textile Strain Sensors

Recognizing Upper Body Postures using Textile Strain Sensors

Healthcare in the Smart Home: A Study of Past, Present and Future

Activity Recognition Using Complex Network Analysis

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