Estimation of Knee Joint Angle Using a Fabric-Based Strain Sensor and Machine Learning: A Preliminary Investigation

Fatigue is a multifunctional and complex phenomenon that affects how individuals perform an activity. Fatigue during running causes changes in normal gait parameters and increases the risk of injury. To address this problem, wearable sensors have been proposed as an unobtrusive and portable system to measure changes in human movement as a result of fatigue. Recently, a category of wearable devices that has gained attention is flexible textile strain sensors because of their ability to be woven into garments to measure kinematics. This study uses flexible textile strain sensors to continuously monitor the kinematics during running and uses a machine learning approach to estimate the level of fatigue during running. Five female participants used the sensor-instrumented garment while running to a state of fatigue. In addition to the kinematic data from the flexible textile strain sensors, the perceived level of exertion was monitored for each participant as an indication of their actual fatigue level. A stacked random forest machine learning model was used to estimate the perceived exertion levels from the kinematic data. The machine learning algorithm obtained a root mean squared value of 0.06 and a coefficient of determination of 0.96 in participant-specific scenarios. This study highlights the potential of flexible textile strain sensors to objectively estimate the level of fatigue during running by detecting slight perturbations in lower extremity kinematics. Future iterations of this technology may lead to real-time biofeedback applications that could reduce the risk of running-related overuse injuries.

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fatigue Monitoring in Running Using Flexible Textile Wearable Sensors

Gholami

Napier

Patiño

et al. 2020

“…The sensor shows similar properties as the commercially available sensors from Adafruit (New York, NY, USA) [ 19 ] and Image SI (Staten Island, NY, USA) [ 20 ], but only has a diameter of 0.4 mm, which makes it suitable to integrate into garments ( Figure 1 ). Previous work has shown good results in using machine learning to obtain accurate measurements from these textile-based stretch sensors [ 21 , 22 ] and using them for the monitoring of human movements [ 15 , 16 , 23 ].…”

Section: Methodsmentioning

confidence: 99%

Detection of Talking in Respiratory Signals: A Feasibility Study Using Machine Learning and Wearable Textile-Based Sensors

Ejupi

Menon

2018

Social isolation and loneliness are major health concerns in young and older people. Traditional approaches to monitor the level of social interaction rely on self-reports. The goal of this study was to investigate if wearable textile-based sensors can be used to accurately detect if the user is talking as a future indicator of social interaction. In a laboratory study, fifteen healthy young participants were asked to talk while performing daily activities such as sitting, standing and walking. It is known that the breathing pattern differs significantly between normal and speech breathing (i.e., talking). We integrated resistive stretch sensors into wearable elastic bands, with a future integration into clothing in mind, to record the expansion and contraction of the chest and abdomen while breathing. We developed an algorithm incorporating machine learning and evaluated its performance in distinguishing between periods of talking and non-talking. In an intra-subject analysis, our algorithm detected talking with an average accuracy of 85%. The highest accuracy of 88% was achieved during sitting and the lowest accuracy of 80.6% during walking. Complete segments of talking were correctly identified with 96% accuracy. From the evaluated machine learning algorithms, the random forest classifier performed best on our dataset. We demonstrate that wearable textile-based sensors in combination with machine learning can be used to detect when the user is talking. In the future, this approach may be used as an indicator of social interaction to prevent social isolation and loneliness.

“…The combination of different IMUs, placed on connected body segments, and the additional information on the kinematic constraints (biomechanical models) enable the estimation of joint angles. As an alternative, e-textile solutions for wearable motion monitoring have been developed [25,26,27]. Textile-based solutions have the advantages of being low cost, lightweight, of low thickness, flexibility, and the possibility of adaptation to different body structures.…”

Section: Background and Related Workmentioning

confidence: 99%

ePhysio: A Wearables-Enabled Platform for the Remote Management of Musculoskeletal Diseases

Vallati

Virdis

Gesi³

et al. 2018

Technology advancements in wireless communication and embedded computing are fostering their evolution from standalone elements to smart objects seamlessly integrated in the broader context of the Internet of Things. In this context, wearable sensors represent the building block for new cyber-physical social systems, which aim at improving the well-being of people by monitoring and measuring their activities and provide an immediate feedback to the users. In this paper, we introduce ePhysio, a large-scale and flexible platform for sensor-assisted physiotherapy and remote management of musculoskeletal diseases. The system leverages networking and computing tools to provide real-time and ubiquitous monitoring of patients. We propose three use cases which differ in scale and context and are characterized by different human interactions: single-user therapy, indoor group therapy, and on-field therapy. For each use case, we identify the social interactions, e.g., between the patient and the physician and between different users and the performance requirements in terms of monitoring frequency, communication, and computation. We then propose three related deployments, highlighting the technologies that can be applied in a real system. Finally, we describe a proof-of-concept implementation, which demonstrates the feasibility of the proposed solution.