Wearable sensing devices for upper limbs: A systematic review

Shoulder Proprioception: A Review

The purpose of this review is to provide a comprehensive resource for shoulder proprioception assessment and its integration into clinical decision making as well as targeted rehabilitation protocols. Data for this review were acquired from peer-reviewed articles from computerized online databases, namely PubMed and Medline, published between 1906 and 2021. The development of digital/smart phone goniometers can improve shoulder joint range of motion (ROM) measurements and demonstrate comparable measurement accuracy to the universal standard goniometer. The inclinometer offers a portable and cost-effective method for measuring shoulder joint angles and arcs of motion in the vertical plane. Two types of dynamometers, the computerized isokinetic machine and the handheld hydraulic dynamometer, are reliable tools for objective shoulder rotator cuff strength assessment. Motion analysis systems are highly advanced modalities that create three-dimensional models of motion arcs using a series of cameras and reflective beads, offering unparalleled precision in shoulder proprioception measurement; however, they require time-consuming calibration and skilled operators. Advancements in wearable devices and compact mobile technology such as iPhone applications may make three-dimensional motion analysis more affordable and practical for outpatient settings in the future. The complex interplay between proprioception and shoulder dysfunction is not fully understood; however, shoulder proprioception can likely both contribute to and be caused by shoulder pathology. In patients with rotator cuff tears, glenohumeral osteoarthritis, and shoulder instability, clinicians can track proprioception to understand a patient's disease progression or response to treatment. Finally, rehabilitation programs targeting shoulder proprioception have shown promising initial results in restoring function and returning athletes to play.

Evaluating the Accuracy of Upper Limb Movement in the Sagittal Plane among Computer Users during the COVID-19 Pandemic

(1) Background: The most common musculoskeletal pathology among healthcare professionals is neck and/or shoulder pain. The aim of this study was to determine the dominant upper limb functionality concerning the ability to replicate a given movement pattern among employees reporting neck or upper limb pain while using a computer during the COVID-19 pandemic. (2) Methods: The study was conducted from March to April 2021 on a group of 45 medical employees who used a computer workstation for 4 to 6 h of their working time. In the design of this study, three study groups were created: a group of patients with pain syndrome of segment C5/C7 of the spine, a group of patients with shoulder pain syndrome, and a control group of healthy volunteers. (3) Results: The examined groups significantly differed in the correctness of performing the given movement (p = 0.001) and the minimum value of inclination during the exercise session (p = 0.026), as well as the maximum lowering (p = 0.03) in relation to the control group. (4) Conclusions: The VECTIS device can be used to assess the accuracy of reflecting the prescribed movement of the upper limb in rehabilitation programs for patients with cervical spine pain syndrome and shoulder pain syndrome.

Feasibility of Using a GENEActiv Accelerometer with Triaxial Acceleration and Temperature Sensors to Monitor Adherence to Shoulder Sling Wear Following Surgery

BACKGROUND

Self-reported adherence to sling wear is unreliable due to recall bias. We aim to assess the feasibility and accuracy of quantifying sling wear and non-wear utilising slings pre-fitted with a GENEActiv accelerometer that houses triaxial acceleration and temperature sensors.

METHODS

Ten participants were asked to wear slings for 480 min (8 h) incorporating 180 min of non-wear time in durations varying from 5-120 min. GENEActiv devices were fitted in sutured inner sling pockets and participants logged sling donning and doffing times. An algorithm based on variability in acceleration in three axes and temperature change was developed to identify sling wear and non-wear and compared to participants' logs.

RESULTS

There was no significant difference between algorithm detected non-wear duration (mean ± standard deviation = 172.0 ± 6.8 min/participant) and actual non-wear (179.7 ± 1.0 min/participant). Minute-by-minute agreement of sensor-detected wear and non-wear with participant reported wear was 97.3 ± 1.5% (range = 93.9-99.0), with mean sensitivity 94.3 ± 3.5% (range = 86.1-98.3) and specificity 99.1 ± 0.8% (range = 93.7-100).

CONCLUSION

An algorithm based on accelerometer-assessed acceleration and temperature can accurately identify shoulder sling wear/non-wear times. This method may have potential for assessing whether sling wear adherence after shoulder surgeries have any bearing on patient functional outcomes.

Sensor Fusion-Based Teleoperation Control of Anthropomorphic Robotic Arm

Sensor fusion is a technique that combines information from multiple sensors in order to improve the accuracy and reliability of the data being collected. In the context of teleoperation control of an anthropomorphic robotic arm, sensor fusion technology can be used to enhance the precise control of anthropomorphic robotic arms by combining data from multiple sensors, such as cameras, data gloves, force sensors, etc. By fusing and processing this sensing information, it can enable real-time control of anthropomorphic robotic arms and dexterous hands, replicating the motion of human manipulators. In this paper, we present a sensor fusion-based teleoperation control system for the anthropomorphic robotic arm and dexterous hand, which utilizes a filter to fuse data from multiple sensors in real-time. As such, the real-time perceived human arms motion posture information is analyzed and processed, and wireless communication is used to intelligently and flexibly control the anthropomorphic robotic arm and dexterous hand. Finally, the user is able to manage the anthropomorphic operation function in a stable and reliable manner. We also discussed the implementation and experimental evaluation of the system, showing that it is able to achieve improved performance and stability compared to traditional teleoperation control methods.

A Self-Powered Triboelectric Nanogenerator Based on Intelligent Interactive System for Police Shooting Training Monitoring and Virtual Reality Interaction

A self-powered triboelectric nanogenerator (SPTENG) based on triboelectric effect and an intelligent interactive system are fabricated for monitoring shooting training and virtual training. The SPTENG is composed of latex and PTFE and an intelligent system. Based on triboelectric effect, the SPTENG can be used to monitor the progress of trigger pressing without a power supply (this is supplied by trigger movements). Because of the flexible properties, it can be attached to a trigger conveniently to monitor the progress of trigger pressing, such as trigger time, trigger stability, etc. Meanwhile, as part of an intelligent shooting system, police can formulate a standard scheme according to signals to improve their skills. Furthermore, they can use it to train between reality and virtuality. Therefore, it has a wide development space in human-computer interaction and real-time information processing.

Virtual interaction and manipulation control of a hexacopter through hand gesture recognition from a data glove

The purpose of this study is to realize virtual interaction and manipulation control of a hexacopter based on hand gesture recognition from a designed data glove, to provide an intuitive and visual real-time simulation system for flight control algorithm verification and external control equipment testing. First, the hand gesture recognition from a designed data glove is studied, which can recognize different actions, such as mobile ready, grab, loosen, landing, take-off, and hover. Then, the design of virtual simulation system for hexacopter capture is completed, with the model design of hexacopter and manipulator, and the simulation software design with $CoppeliaSim$ . Finally, virtual simulation experiment of hexacopter grasping and virtual flight control experiment based on data glove are tested, respectively, and quantitatively described. The overall recognition rate is 84.3%, indicating that the data glove produced has the ability to recognize gestures, but its recognition performance is not superior. In gesture recognition, the recognition rate of static gestures is relatively higher than that of dynamic gestures. Among the static gestures, the hover gesture has the highest recognition rate. The average correct rate of static gestures can reach 94%. The lowest recognition rate of dynamic gestures is upward movement, and the average recognition rate of dynamic gestures is 76.1%. The research can be used to remotely operate hexacopter using a data glove in the future and improve the control performance through virtual interaction and manipulation simulation before actual application.

Towards Human Stress and Activity Recognition: A Review and a First Approach Based on Low-Cost Wearables

Detecting stress when performing physical activities is an interesting field that has received relatively little research interest to date. In this paper, we took a first step towards redressing this, through a comprehensive review and the design of a low-cost body area network (BAN) made of a set of wearables that allow physiological signals and human movements to be captured simultaneously. We used four different wearables: OpenBCI and three other open-hardware custom-made designs that communicate via bluetooth low energy (BLE) to an external computer—following the edge-computingconcept—hosting applications for data synchronization and storage. We obtained a large number of physiological signals (electroencephalography (EEG), electrocardiography (ECG), breathing rate (BR), electrodermal activity (EDA), and skin temperature (ST)) with which we analyzed internal states in general, but with a focus on stress. The findings show the reliability and feasibility of the proposed body area network (BAN) according to battery lifetime (greater than 15 h), packet loss rate (0% for our custom-made designs), and signal quality (signal-noise ratio (SNR) of 9.8 dB for the ECG circuit, and 61.6 dB for the EDA). Moreover, we conducted a preliminary experiment to gauge the main ECG features for stress detection during rest.

A Wearable IMU System for Flexible Teleoperation of a Collaborative Industrial Robot

Increasing the accessibility of collaborative robotics requires interfaces that support intuitive teleoperation. One possibility for an intuitive interface is offered by wearable systems that measure the operator’s movement and use the information for robot control. Such wearable systems should preserve the operator’s movement capabilities and, thus, their ability to flexibly operate in the workspace. This paper presents a novel wireless wearable system that uses only inertial measurement units (IMUs) to determine the orientation of the operator’s upper body parts. An algorithm was developed to transform the measured orientations to movement commands for an industrial collaborative robot. The algorithm includes a calibration procedure, which aligns the coordinate systems of all IMUs, the operator, and the robot, and the transformation of the operator’s relative hand motions to the movement of the robot’s end effector, which takes into account the operator’s orientation relative to the robot. The developed system is demonstrated with an example of an industrial application in which a workpiece needs to be inserted into a fixture. The robot’s motion is compared between the developed system and a standard robot controller. The results confirm that the developed system is intuitive, allows for flexible control, and is robust enough for use in industrial collaborative robotic applications.

Current Advances in Wearable Devices and Their Sensors in Patients With Depression

In this study, a literature survey was conducted of research into the development and use of wearable devices and sensors in patients with depression. We collected 18 studies that had investigated wearable devices for assessment, monitoring, or prediction of depression. In this report, we examine the sensors of the various types of wearable devices (e.g., actigraphy units, wristbands, fitness trackers, and smartwatches) and parameters measured through sensors in people with depression. In addition, we discuss future trends, referring to research in other areas employing wearable devices, and suggest the challenges of using wearable devices in the field of depression. Real-time objective monitoring of symptoms and novel approaches for diagnosis and treatment using wearable devices will lead to changes in management of patients with depression. During the process, it is necessary to overcome several issues, including limited types of collected data, reliability, user adherence, and privacy concerns.