On-body Placement Research Articles

Five factors affecting the on-body placement of wearable tactile safety promotion device for construction workers-on-foot

PurposeTactile communication that relies on the human sense of touch replicated using vibration motors is increasingly being explored for seamless communication on construction jobsite. However, the technological efficacy cannot secure the users’ acceptability of the tactile communication devices. This study aims to assess the factors affecting the wearability of such a portable tactile device based on the responses from practicing professionals.Design/methodology/approachThe investigation adapted a three-step phenomenological interviewing approach to seek feedback from construction personnel in Texas, the USA, regarding the viability of wearable tactile communication. The interviewees expressed various opinions about the on-body placement upon exposure to a portable tactile feedback prototype developed for this study, which was used to derive inferences regarding the factors affecting its on-field acceptability.FindingsAll the participants of the round-table study (11 out of 11) considered tactile feedback as a viable mode of communication on construction jobsite. Seven professionals supported the integration of a tactile device with the hard hat, whereas the rest preferred tactile eyeglasses. Weatherability, rechargeability, traceability, safety and social receptivity were identified as the major factors affecting the on-body placement of the wearable tactile communication device.Originality/valueThis paper presents a roadmap to gain construction industry opinion on the factors that can affect the on-body placement of a wearable tactile communication device. The five aforementioned factors impacting tactile communication acceptability were used to evaluate 10 potential on-body placements. The findings have implications for research and development of wearable tactile devices and the subsequent acceptability of such a device on the jobsite.

On-Body Placement of Wearable Safety Promotion Devices Based on Wireless Communication for Construction Workers-on-Foot: State-of-the-Art Review.

High auditory noise levels and limited visibility are often considered among the main factors that hinder seamless communication on construction sites. Many previous research studies have leveraged technology to overcome these obstacles and communicate using the hearing, sight and touch senses. However, the technological efficacy does not secure the users’ perceptivity of the wireless communication devices. Statistical data regarding the number of fatal accidents on construction sites have remained steady despite regular efforts. This study analyzed prior research on wearable safety promotion devices for personnel that move around the jobsite on foot. A seven-point checklist was utilized to shortlist prior studies (2005–2021) attempting to provide safety information wirelessly to the construction workers-on-foot. The reasoning behind various on-body placements was investigated along with the information conveyed using the three communication modalities. A novel communication network is also introduced to visualize the technical details. Lastly, limitations and future recommendations have been presented to gain insights about the factors that might affect the placement of the wearable safety promotion devices.

Improving the imaging performance of the 1.5 T MR-linac using a flexible, 32-channel, on-body receive array

High impedance coils (HICs) are suitable as a building block of receive arrays for MRI-guided radiotherapy (MRIgRT) as HICs do not require radiation-attenuating capacitors and dense support materials. Recently, we proved the feasibility of using HICs to create a radiation transparent (i.e. radiolucent) window. In this work, we constructed a fully functional 32-channel array based on this design. The anterior element is flexible and follows the shape of the subject, while the posterior element is rigid to support the subject. Both elements feature a 2 × 8 channel layout. Here, we discuss the construction process and characterize the array’s radiolucency and imaging performance. The dosimetric impact of the array was quantified by assessing the surface dose increase and attenuation of a single beam. The imaging performance of the prototype was compared to the clinical array in terms of visual appearance, signal-to-noise ratio (SNR), and acceleration performance, both in phantom and in-vivo measurements. Dosimetry measurements showed that on-body placement changed the anterior and posterior surface dose by +3% and −16% of the dose maximum. Attenuation under the anterior support materials and conductors was 0.3% and ≤1.5%, respectively. Phantom and in-vivo imaging with this array demonstrated an improvement of the SNR at the surface and the image quality in general. Simultaneous irradiation did not affect the SNR. G-factors were reduced considerably and clinically used sequences could be accelerated by up to 45%, which would greatly reduce pre-beam imaging times. Finally, the maximally achievable temporal resolution of abdominal 3D cine imaging was improved to 1.1 s, which was > 5 × faster than could be achieved with the clinical array. This constitutes a big step towards the ability to resolve respiratory motion in 3D. In conclusion, the proposed 32-channel array is compatible with MRIgRT and can significantly reduce scan times and/or improve the image quality of all on-line scans.

Impact of on‐body IMU placement on inertial navigation

Even though technology-aided personal navigation is an extensively studied research topic, approaches based on inertial sensors remain challenging. In this study, the authors present a comparison between different inertial systems, investigating the impacts of on-body placement of Inertial Measurement Units (IMUs) and, consequently, of different algorithms for the estimation of the travelled path on the navigation accuracy. In particular, the system performance is investigated considering two IMU placements: (i) on the feet and (ii) on the lower back. Sensor fusion is then considered in order to take advantage of the strengths of each placement. The results are validated through an extensive data collection in indoor and outdoor environments.

STRIDE VARIABILITY MEASURES DERIVED FROM WRIST AND HIP-WORN ACCELEROMETERS

Many epidemiological and clinical studies use accelerometry to objectively measure physical activity using the activity counts, vector magnitude, or number of steps. These measures use just a fraction of the information in the raw accelerometry data as they are typically summarized at the minute level. To address this problem, we define and estimate two measures of temporal stride-to-stride gait variability based on raw accelerometry data: Amplitude Deviation (AD) and Phase Deviation (PD). We explore the sensitivity of our approach to on-body placement of the accelerometer by comparing hip, left and right wrist placements. We illustrate the approach by estimating AD and PD in 46 elderly participants in the Developmental Epidemiologic Cohort Study (DECOS) who worn accelerometers during a 400m walk test. We also show that AD and PD have a statistically significant association with the gait speed and sit-to-stand test performance.

Stride variability measures derived from wrist- and hip-worn accelerometers

Many epidemiological and clinical studies use accelerometry to objectively measure physical activity using the activity counts, vector magnitude, or number of steps. These measures use just a fraction of the information in the raw accelerometry data as they are typically summarized at the minute level. To address this problem, we define and estimate two measures of temporal stride-to-stride gait variability based on raw accelerometry data: Amplitude Deviation (AD) and Phase Deviation (PD). We explore the sensitivity of our approach to on-body placement of the accelerometer by comparing hip, left and right wrist placements. We illustrate the approach by estimating AD and PD in 46 elderly participants in the Developmental Epidemiologic Cohort Study (DECOS) who worn accelerometers during a 400m walk test. We also show that AD and PD have a statistically significant association with the gait speed and sit-to-stand test performance.

Vertical and horizontal transmission enhancement between antennas using textile artificial magnetic conductor waveguide sheet

A textile artificial magnetic conductor (AMC) waveguide sheet with a textile diamond dipole is proposed to improve wireless on-body transmission. The sheet-like waveguide provides an independent transmission path which can reduce the performance degradation caused by the human body. The proposed AMC sheet is made entirely of textiles for both the substrate and the conducting parts, thus making it suitable for wearable communications. Transmission characteristics between antennas are studied rigorously in free space and on-body environments. Significant transmission enhancement is observed for all the tested orientations and positions for both free space and on-body placements. With the presence of the AMC, the S 21 has peaks of up to a maximum of −9.9 dB. This textile AMC sheet constitutes a new approach for efficient wearable wireless body-centric communications.

Sensor Placement Variations in Wearable Activity Recognition

This article explores how placement variations in user-carried electronic appliances influence human action recognition and how such influence can be mitigated. The authors categorize possible variations into three classes: placement on different body parts (such as a jacket pocket versus a hip holster versus a trouser pocket), small displacement within a given coarse location (such as a device shifting in a pocket), and different orientations. For each of these variations, they present a systematic evaluation of the impact on human action recognition and give an overview of possible approaches to deal with them. They conclude with a detailed practical example on how to compensate for on-body placements variations that builds on an extension of their previous work. This article is part of a special issue on wearable computing.

A single vs. multi-sensor approach to enhanced detection of smartphone placement.

In this paper, the authors evaluate the ability to detect on-body device placement of smartphones. A feasibility study is undertaken with N=5 participants to identify nine key locations, including in the hand, thigh and backpack, using a multitude of commonly available smartphone sensors. Sensors examined include the accelerometer, magnetometer, gyroscope, pressure and light sensors. Each sensor is examined independently, to identify the potential contributions it can offer, before a fused approach, using all sensors is adopted. A total of 139 features are generated from these sensors, and used to train five machine learning algorithms, i.e. C4.5, CART, Naïve Bayes, Multilayer Perceptrons, and Support Vector Machines. Ten-fold cross validation is used to validate these models, achieving classification results as high as 99%.

Energy-Efficient Real-Time Human Mobility State Classification Using Smartphones

The key benefits of using the smartphone accelerometer for human mobility analysis, with or without location determination based upon GPS, Wi-Fi or GSM is that it is energy-efficient, provides real-time contextual information and has high availability. Using measurements from an accelerometer for human mobility analysis presents its own challenges as we all carry our smartphonesdifferently and the measurements are body placement dependent. Also it often relies on an on-demand remote data exchangefor analysis and processing; which is less energy-efficient, has higher network costs and is not real-time. We present a novelaccelerometer framework based upon a probabilistic algorithm that neutralizes the effect of different smartphone on-body placements and orientations to allow human movements to be more accurately and energy-efficiently identified. Using solely the embeddedsmartphone accelerometer without need for referencing historical data and accelerometer noise filtering, our method can in real-time with a time constraint of 2 seconds identify the human mobility state. The method achieves an overall average classification accuracyof 92 percent when evaluated on a dataset gathered from fifteen individuals that classified nine different urban human mobility states.

A smart wearable textile array system for biomedical telemetry applications

A smart wearable textile array system (SWTAS) with direction of arrival (DoA) estimation and beamforming is proposed and developed for biomedical telemetry applications. This conformal system enables effective and continuous patient monitoring when combined with one or more health sensors, as information about the subject's health condition is received adaptively to guarantee link reliability. This operation is facilitated by a receiver front-end and a digital baseband beamforming network, which enables scalability and flexibility. The proposed SWTAS also features flexible antenna arrays made using textiles, which are arbitrarily located on a cylindrically shaped body phantom to ensure wide spatial DoA estimation capability. Besides being designed to suit on-body placement, the system performance is also characterized for on-body usage using a commercial body-emulating liquid, and placed at a realistic distance from the body, considering user clothing. Investigation indicated a good performance in the system's ±80° forward plane with a DoA accuracy of 3° . Finally, a practical evaluation is presented using two transmitters placed at distinct locations and distances. The system successfully estimated both DoAs and received the telemetry signals using beamforming.

A Wearable Active Antenna for Global Positioning System and Satellite Phone

A wearable multiband circularly polarized active antenna is presented for use in Global Positioning System and Iridium satellite phone applications. The square patch antenna is constructed using flexible foam and fabric substrates and conductors etched on thin copper-on-polyimide films. The feed substrate integrates a compact low-noise amplifier chip directly underneath the antenna patch. The antenna performance is studied under bending conditions and in the presence of a human body. The active antenna exhibits a gain higher than 25 dBi and a 3 dB axial ratio bandwidth exceeding 183 MHz in free-space conditions and is robust to bending and on-body placement.

TEXTILE ARTIFICIAL MAGNETIC CONDUCTOR WAVEGUIDE JACKET FOR ON-BODY TRANSMISSION ENHANCEMENT

Diamond dipole antennas with textile Artiflcial Magnetic Conductor (AMC) sheet-like waveguide were designed to investigate the possibility of improving transmission between antennas. Fleece fabric is used as the substrate of the textile AMC while SHIELDIT fabric is used as the conductive patches and ground plane. The AMC surface is designed to resonate at 2.45GHz with the goal to enhance the transmission between antennas. A small textile AMC waveguide sheet as well as an AMC waveguide jacket were fabricated. The S-parameters performance of the two antennas with textile AMC sheet was investigated in a free space environment and near to human body setting. The efiect of difierent antennas' orientations was also considered. Measurements were conducted thoroughly to validate the simulated flndings. Compared to free space environment, S21 transmission between two dipoles is improved up to maximum i10dB when having textile AMC waveguide sheet beneath them. For both ofi-body and on-body placements, signiflcant transmission enhancement has been observed with the introduction of the AMC sheet-like waveguide. Directive radiation patterns with high gain have also been achieved with the proposed AMC waveguide jacket.

Designing and sharing activity recognition systems across platforms

Features Concept Matching Self-Calibration, Self-Learning Figure 7. Representation of the level at which a common representation is assumed to share a recognition system between users (platforms) or domains. 90 • IEEE ROBOTICS & AUTOMATION MAGAZINE • JUNE 2011 A hybrid approach between sensor-level and feature-level sharing was further proposed by Kunze et al., who demonstrated that sensors can autonomously self-characterize their on-body placement [55] and orientation [56] using machine-learning techniques. They propose to use onbody sensor placement self-characterization as a way to select, among a number of preprogrammed ARCs, the one most suited for the detected sensor placement. Similarly in robotics, data from different sensors can be converted into identical abstract representations. For instance, 3-D point clouds can be measured by stereovision or a laser-range finder. Classifier-Level Sharing Transfer learning allows to translate a classification problem from one feature space to another [57] and was used to transfer perceptual categories across modalities in biological and artificial systems [58]. Conceptually, transfer learning may thus be used to translate the capability to recognize activities from one platform to another without enforcing a similar input space (i.e., sensors and features). Thus, the transfer does not affect higher-level reasoning. Practical principles allowing a system A to confer activity-recognition capabilities to another system B are outlined in [19]. Each system A and B is composed of a set of sensors SA, SB, ARCs ARCA, ARCB, and a unified communication protocol. The process of transfer learning works as follows (see Figure 8). l The user employs an activity-aware system A with ARCA and sensor set SA. For instance, a set of instrumented drawers is capable of reporting which one is being opened or closed in a storage-management scenario. l A new system is deployed in the user’s personal area network comprising a set of unknown new sensors SB (on body and/or in the user’s surroundings) and an untrained ARCB. For instance, the user wears a new sensorized wristband with an integrated acceleration sensor. l As the user performs activities, the ARCA recognizes them and broadcasts this information. l The new system B receives the class labels of the recognized activities. The ARCB incrementally learns the mapping between the signals of the sensor set SB and the activity classes. l Eventually, the systemA can be removed. The activity-recognition capability is now entirely provided by the system B. The underlying assumptions are the two systems that coexist for a longer time to operate transfer learning. In Figure 8, we show that, as the user interacts with a set of drawers, the body-worn system incrementally learns to recognize opening and closing gestures. In robotics, this sharing approach may be used to allow the robots with different sensory inputs to learn to recognize semantically identical activities or to learn how to use a new sensor when the robot parts are upgraded, thus easing programming. Symbolic-Level Sharing The reasoning program to infer higher level activities from spotted action primitives is shared between platforms. As the environment in which the two platforms operate may lead to the detection of semantically different action primitives, a direct transfer of the reasoning is not always possible. Carrying out a prior concept matching can address this. For instance, to reason about the activity of a user, one needs first to know in which room he is located. One environment may have a sensor allowing to detect the action primitive “room door activated.” Another environment may have a proximity infrared sensor allowing to detect “movement in the room.” The interpretation of the sensor data requires different features and classifiers in each case. However, although the classifiers deliver semantically different action primitives, Recognized Activities from Drawer Sensors Recognized Activities from On-Body Sensors