Wrap-Around Wearable Coils for Seamless Monitoring of Joint Flexion

Wearable Loop Sensors for Knee Flexion Monitoring: Dynamic Measurements on Human Subjects

Goals: We have recently introduced a new class of wearable loop sensors for joint flexion monitoring that overcomes limitations in the state-of-the-art. Our previous studies reported a proof-of-concept on a cylindrical phantom limb, under static scenarios and with a rigid sensor. In this work, we evaluate our sensors, for the first time, on human subjects, under dynamic scenarios, using a flexible textile-based prototype tethered to a network analyzer. An untethered version is also presented and validated on phantoms, aiming towards a fully wearable design. Methods: Three dynamic activities (walking, brisk walking, and full flexion/extension, all performed in place) are used to validate the tethered sensor on ten (10) adults. The untethered sensor is validated upon a cylindrical phantom that is bent manually at random speed. A calibration mechanism is developed to derive the sensor-measured angles. These angles are then compared to gold-standard angles simultaneously captured by a light detection and ranging (LiDAR) depth camera using root mean square error (RMSE) and Pearson's correlation coefficient as metrics. Results: We find excellent correlation (≥ 0.981) to gold-standard angles. The sensor achieves an RMSE of 4.463° ± 1.266° for walking, 5.541° ± 2.082° for brisk walking, 3.657° ± 1.815° for full flexion/extension activities, and 0.670° ± 0.366° for the phantom bending test. Conclusion: The tethered sensor achieves similar to slightly higher RMSE as compared to other wearable flexion sensors on human subjects, while the untethered version achieves excellent RMSE on the phantom model. Concurrently, our sensors are reliable over time and injury-safe, and do not obstruct natural movement. Our results set the ground for future improvements in angular resolution and for realizing fully wearable designs, while maintaining the abovementioned advantages over the state-of-the-art.

Movement Recognition through Inductive Wireless Links: Investigation of Different Fabrication Techniques

In this paper, an inductive wireless link for motion recognition is investigated. In order to validate the feasibility of a wearable implementation, the use of three different materials is analyzed: a thin copper wire, a conductive yarn, and a conductive non-woven fabric. Results from the application of the developed devices on an arm are reported and discussed. It is demonstrated that the proposed textile inductive resonant wireless links are well suited for developing a compact wearable system for joint flexion recognition.

A Simple Low-Cost Wearable Sensor for Long-Term Ambulatory Monitoring of Knee Joint Kinematics

OBJECTIVE

Accurate monitoring of joint kinematics in individuals with neuromuscular and musculoskeletal disorders within ambulatory settings could provide important information about changes in disease status and the effectiveness of rehabilitation programs and/or pharmacological treatments. This paper introduces a reliable, power efficient, and low-cost wearable system designed for the long-term monitoring of joint kinematics in ambulatory settings.

METHODS

Seventeen healthy subjects wore a retractable string sensor, fixed to two anchor points on the opposing segments of the knee joint, while walking at three different self-selected speeds. Joint angles were estimated from calibrated sensor values and their derivatives in a leave-one-subject-out cross validation manner using a random forest algorithm.

RESULTS

The proposed system estimated knee flexion/extension angles with a root mean square error (RMSE) of 5.0°±1.0° across the study subjects upon removal of a single outlier subject. The outlier was likely a result of sensor miscalibration.

CONCLUSION

The proposed wearable device can accurately estimate knee flexion/extension angles during locomotion at various walking speeds.

SIGNIFICANCE

We believe that our novel wearable technology has great potential to enable joint kinematic monitoring in ambulatory settings and thus provide clinicians with an opportunity to closely monitor joint recovery, develop optimal, personalized rehabilitation programs, and ultimately maximize therapeutic outcomes.

Modeling Fabric Movement for Future E-Textile Sensors

Studies with e-textile sensors embedded in garments are typically performed on static and controlled phantom models that do not reflect the dynamic nature of wearables. Instead, our objective was to understand the noise e-textile sensors would experience during real-world scenarios. Three types of sleeves, made of loose, tight, and stretchy fabrics, were applied to a phantom arm, and the corresponding fabric movement was measured in three dimensions using physical markers and image-processing software. Our results showed that the stretchy fabrics allowed for the most consistent and predictable clothing-movement (average displacement of up to −2.3 ± 0.1 cm), followed by tight fabrics (up to −4.7 ± 0.2 cm), and loose fabrics (up to −3.6 ± 1.0 cm). In addition, the results demonstrated better performance of higher elasticity (average displacement of up to −2.3 ± 0.1 cm) over lower elasticity (average displacement of up to −3.8 ± 0.3 cm) stretchy fabrics. For a case study with an e-textile sensor that relies on wearable loops to monitor joint flexion, our modeling indicated errors as high as 65.7° for stretchy fabric with higher elasticity. The results from this study can (a) help quantify errors of e-textile sensors operating “in-the-wild,” (b) inform decisions regarding the optimal type of clothing-material used, and (c) ultimately empower studies on noise calibration for diverse e-textile sensing applications.