Preferred Placement and Usability of a Smart Textile System vs. Inertial Measurement Units for Activity Monitoring

Gait analysis has been widely studied by researchers due to the impact in clinical fields. It provides relevant information on the condition of a patient’s pathologies. In the last decades, different gait measurement methods have been developed in order to identify parameters that can contribute to gait cycles. Analyzing those parameters, it is possible to segment and identify different phases of gait cycles, making these studies easier and more accurate. This paper proposes a simple gait segmentation method based on plantar pressure measurement. Current methods used by researchers and clinicians are based on multiple sensing devices (e.g., multiple cameras, multiple inertial measurement units (IMUs)). Our proposal uses plantar pressure information from only two sensorized insoles that were designed and implemented with eight custom-made flexible capacitive sensors. An algorithm was implemented to calculate gait parameters and segment gait cycle phases and subphases. Functional tests were performed in six healthy volunteers in a 10 m walking test. The designed in-shoe insole presented an average power consumption of 44 mA under operation. The system segmented the gait phases and sub-phases in all subjects. The calculated percentile distribution between stance phase time and swing phase time was almost 60%/40%, which is aligned with literature reports on healthy subjects. Our results show that the system achieves a successful segmentation of gait phases and subphases, is capable of reporting COP velocity, double support time, cadence, stance phase time percentage, swing phase time percentage, and double support time percentage. The proposed system allows for the simplification of the assessment method in the recovery process for both patients and clinicians.

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confidence: 99%

Gait Segmentation Method Using a Plantar Pressure Measurement System with Custom-Made Capacitive Sensors

Aqueveque

Germany

Osorio

et al. 2020

“…Continuous monitoring of knee motion under daily environments is necessary for rehabilitation and daily care of patients. However, only a few works provided good results of knee motion under daily conditions [4,16,17]. Our flexible sensor was made of fabric with good accuracy, which can be embedded in garments without affecting the daily activities of humans.…”

Section: Resultsmentioning

confidence: 99%

A Textile Sensor for Long Durations of Human Motion Capture

Dai

Dong

et al. 2019

Human posture and movement analysis is important in the areas of rehabilitation, sports medicine, and virtual training. However, the development of sensors with good accuracy, low cost, light weight, and suitability for long durations of human motion capture is still an ongoing issue. In this paper, a new flexible textile sensor for knee joint movement measurements was developed by using ordinary fabrics and conductive yarns. An electrogoniometer was adopted as a standard reference to calibrate the proposed sensor and validate its accuracy. The knee movements of different daily activities were performed to evaluate the performance of the sensor. The results show that the proposed sensor could be used to monitor knee joint motion in everyday life with acceptable accuracy.

“…Accuracy of the current STS was found to be at least comparable to the earlier studies using different wearable activity monitors. It should also be noted that participants may prefer using SGs rather than systems based on inertial measurement units [41]. There are several possible explanations for this high accuracy in identifying diverse physical activities.…”

Section: Discussionmentioning

confidence: 99%

“…The sampling rate for the SUS was 1000 Hz. We purposefully selected an undershirt, in accordance with a recent study indicating that participants preferred a short-sleeved T-shirt over other types of SGs [41]. Furthermore, the upper body typically involves a larger range of activity, as well as variability, compared to the lower body, and was thus thought to be more effective in classifying physical activities.…”

Section: Methodsmentioning

confidence: 99%

“…An instrumented shoe equipped with special insoles and an inertial measurement unit (IMU) recognizes a number of human movements (sit-to-stand, locomotion, and walking type) using the decision tree method, with an overall accuracy of 97% [38]. However, while they appear to be a promising solution in several applications [39,40], these wearable sensors are external devices that need to be attached on the body, and therefore they may change the appearance of garment and compromise usability [41].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Classifying Diverse Physical Activities Using “Smart Garments”

Esfahani

Nussbaum

2019

Physical activities can have important impacts on human health. For example, a physically active lifestyle, which is one of the most important goals for overall health promotion, can diminish the risk for a range of physical disorders, as well as reducing health-related expenditures. Thus, a long-term goal is to detect different physical activities, and an important initial step toward this goal is the ability to classify such activities. A recent and promising technology to discriminate among diverse physical activities is the smart textile system (STS), which is becoming increasingly accepted as a low-cost activity monitoring tool for health promotion. Accordingly, our primary aim was to assess the feasibility and accuracy of using a novel STS to classify physical activities. Eleven participants completed a lab-based experiment to evaluate the accuracy of an STS that featured a smart undershirt (SUS) and commercially available smart socks (SSs) in discriminating several basic postures (sitting, standing, and lying down), as well as diverse activities requiring participants to walk and run at different speeds. We trained three classification methods—K-nearest neighbor, linear discriminant analysis, and artificial neural network—using data from each smart garment separately and in combination. Overall classification performance (global accuracy) was ~98%, which suggests that the STS was effective for discriminating diverse physical activities. We conclude that, overall, smart garments represent a promising area of research and a potential alternative for discriminating a range of physical activities, which can have positive implications for health promotion.