- Award ID(s):
- 1646470
- PAR ID:
- 10190248
- Date Published:
- Journal Name:
- International Conference on Distributed Computing in Sensor Systems and workshops
- ISSN:
- 2325-2944
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
Stress increases the risk of several mental and physical health problems like anxiety, hypertension, and cardiovascular diseases. Better guidance and interventions towards mitigating the impact of stress can be provided if stress can be monitored continuously. The recent proliferation of wearable devices and their capability in measuring several physiological signals related to stress have created the opportunity to measure stress continuously in the wild. Wearable devices used to measure physiological signals are mostly placed on the wrist and the chest. Though currently chest sensors, with/without wrist sensors, provide better results in detecting stress than using wrist sensors only, chest devices are not as convenient and prevalent as wrist devices, particularly in the free-living context. In this paper, we present a solution to detect stress using wrist sensors that emulate the gold standard chest sensors. Data from wrist sensors are translated into the data from chest sensors, and the translated data is used for stress detection without requiring the users to wear any device on the chest. We evaluated our solution using a public dataset, and results show that our solution detects stress with accuracy comparable to the gold standard chest devices which are impractical for daily usemore » « less
-
Abstract Highly stretchable fiber sensors have attracted significant interest recently due to their applications in wearable electronics, human–machine interfaces, and biomedical implantable devices. Here, a scalable approach for fabricating stretchable multifunctional electrical and optical fiber sensors using a thermal drawing process is reported. The fiber sensors can sustain at least 580% strain and up to 750% strain with a helix structure. The electrical fiber sensor simultaneously exhibits ultrahigh stretchability (400%), high gauge factors (≈1960), and excellent durability during 1000 stretching and bending cycles. It is also shown that the stretchable step‐index optical fibers facilitate detection of bending and stretching deformation through changes in the light transmission. By combining both electrical and optical detection schemes, multifunctional fibers can be used for quantifying and distinguishing multimodal deformations such as bending and stretching. The fibers’ utility and functionality in sensing and control applications are demonstrated in a smart glove for controlling a virtual hand model, a wrist brace for wrist motion tracking, fiber meshes for strain mapping, and real‐time monitoring of multiaxial expansion and shrinkage of porcine bladders. These results demonstrate that the fiber sensors can be promising candidates for smart textiles, robotics, prosthetics, and biomedical implantable devices.
-
Medication adherence is a major problem in the healthcare industry: it has a major impact on an individual’s health and is a major expense on the healthcare system. We note that much of human activity involves using our hands, often in conjunction with objects. Camera-based wearables for tracking human activities have sparked a lot of attention in the past few years. These technologies have the potential to track human behavior anytime, any place. This paper proposes a paradigm for medication adherence employing innovative wrist-worn camera technology. We discuss how the device was built, various experiments to demonstrate feasibility and how the device could be deployed to detect the micro-activities involved in pill taking so as to ensure medication adherence.more » « less
-
This paper presents a portable inertial measurement unit (IMU)-based motion sensing system and proposed an adaptive gait phase detection approach for non-steady state walking and multiple activities (walking, running, stair ascent, stair descent, squat) monitoring. The algorithm aims to overcome the limitation of existing gait detection methods that are time-domain thresholding based for steady-state motion and are not versatile to detect gait during different activities or different gait patterns of the same activity. The portable sensing suit is composed of three IMU sensors (wearable sensors for gait phase detection) and two footswitches (ground truth measurement and not needed for gait detection of the proposed algorithm). The acceleration, angular velocity, Euler angle, resultant acceleration, and resultant angular velocity from three IMUs are used as the input training data and the data of two footswitches used as the training label data (single support, double support, swing phase). Three methods 1) Logistic Regression (LR), 2) Random Forest Classifier (RF), and 3) Artificial Neural Network (NN) are used to build the gait phase detection models. The result shows our proposed gait phase detection with Random Forest Classifier can achieve 98.94% accuracy in walking, 98.45% in running, 99.15% in stair-ascent, 99.00% in stair-descent, and 99.63% in squatting. It demonstrates that our sensing suit can not only detect the gait status in any transient state but also generalize to multiple activities. Therefore, it can be implemented in real-time monitoring of human gait and control of assistive devices.more » « less
-
Myoelectric control of prostheses is a long-established technique, using surface electromyography (sEMG) to detect user intention and perform subsequent mechanical actions. Most machine learning models utilized in control systems are trained using isolated movements that do not reflect the natural movements occurring during daily activities. Moreover, movements are often affected by arm postures, the duration of activities, and personal habits. It is crucial to have a control system for multi-degree-of-freedom (DoF) prosthetic arms that is trained using sEMG data collected from activities of daily living (ADL) tasks. This work focuses on two major functional wrist movements: pronation-supination and dart-throwing movement (DTM), and introduces a new wrist control system that directly maps sEMG signals to the joint velocities of the multi-DoF wrist. Additionally, a specific training strategy (Quick training) is proposed that enables the controller to be applied to new subjects and handle situations where sensors may displace during daily living, muscles can become fatigued, or sensors can become contaminated (e.g., due to sweat). The prosthetic wrist controller is designed based on data from 24 participants and its performance is evaluated using the Root Mean Square Error (RMSE) and Pearson Correlation. The results are found to depend on the characteristics of the tasks. For example, tasks with dart-throwing motion show smaller RSME values (Hammer: 6.68 deg/s and Cup: 7.92 deg/s) compared to tasks with pronation-supination (Bulb: 43.98 deg/s and Screw: 53.64 deg/s). The proposed control technique utilizing Quick training demonstrates a decrease in the average root mean square error (RMSE) value by 35% and an increase in the average Pearson correlation value by 40% across all four ADL tasks.more » « less