This is an open access article under CC BY license (https://creativecommons.org/licenses/by/4.0/) Background: Effects of internal and external workloads (IL, EL) on lower limb soft-tissue injuries (LLSTI) risk in male soccer players has been described, the relationships remain unclear in collegiate female (soccer players. Objective: The purpose was to examine the mean difference in IL and EL in LLSTI between non-injured and injured groups (N-IG and IG). Method: 20 collegiate female soccer players (age: 19.2±1.2years; height: 168.2±7.3cm; body mass: 41.0±17.9kg) were included for 14 week competitive season. IL included average heart rate (Avg-HR) and high heart rate zone. EL included total distance, average speed (Avg-Spd), and high-speed running distance. Injuries were counted if (a) they were LLSTI and muscular/ ligamentous strains or tears and tendon problems, and (b) the players missed more than one match or training session. Acute (7-day simple average) and chronic (21-day simple average) IL and EL were calculated in the IG while the mean of acute (7-day) and chronic (21-day) IL and EL were computed in the NIG. Acute Chronic Workload Ratio (ACWR) was calculated as the ratio of acute and chronic IL and EL. Results: Seven LLSTI occurred over 14 weeks. The acute Avg-HR and ACWR of Avg-Spd were significantly higher in the IG than the N-IG (p=0.001 and 0.024). IL and EL in the IG were placed below or above the mean of the N-IG. Conclusion: LLSTI might occur at high and low workloads in collegiate female soccer players. This may support the use of micro-technology to monitor workload based on individual player’s threshold to reduce LLSTI.

Yes / Due to the limited communication range of wearable devices, there is the need for wearable devices to communicate amongst themselves, supporting devices and the internet or to the internet. Most wearable devices are not internet enabled and most often need an internet enabled broker device or intermediate device in order to reach the internet. For a secure end to end communication between these devices security measures like authentication must be put in place in other to prevent unauthorised access to information given the sensitivity of the information collected and transmitted. Therefore, there are other existing authentication solutions for wearable devices but these solutions actively involve from time to time the user of the device which is prone to a lot of challenges. As a solution to these challenges, this paper proposes a secure point-to-point Self-authentication mechanism that involves device to device interaction. This work exploits existing standards and framework like NFC, PPP, EAP etc. in other to achieve a device compatible secure authentication protocol amongst wearable device and supporting devices..

BACKGROUND: Prior to now, sleep has been particularly difficult to accurately measure in a home setting. The acceptability of wearable devices that allow for the collection of not only sleep data, but heart rate, activity, and other variables allows for the expansion of research from the lab into a patient’s daily life. METHODS: Data from three studies was evaluated to determine acceptability of Fitbit wearable devices as well as the utility of sleep data the devices could collect to characterize sleep disturbances. The populations included two post-surgical groups experiencing acute pain (orthopedic surgery, n = 7; cardiac surgery n = 14) and one group of children and adolescents receiving intensive treatment for chronic pain (n = 14). RESULTS: Both the composition of the studies and the Fitbit devices were well tolerated by all three groups, as measured by an acceptability survey. For utility, significant differences were found in step counts, number of nightly awakenings, and pain ratings. Correlations were also found between pain and sleepiness, step counts, and sleep efficiency. CONCLUSION: The future is bright for the integration of wearable devices and other smart technology into clinical settings. For the populations studied here, this could mean fewer hospital visits and increased quality of life by being able to provide feedback to providers via validated, non-invasive methods.

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Finger gesture recognition using glove-like interfaces are very accurate for sensing individual finger positions by employing a gamut of sensors. However, for the same reason, they are also very costly, cumbersome and unaesthetic for use in artistic scenarios such as gesture based music composition platforms like Virginia Tech's Linux Laptop Orchestra. Wearable computing has shown promising results in increasing portability as well as enhancing proprioceptive perception of the wearers' body. In this thesis, we present the proof-of-concept for designing a novel muscle-machine interface for interpreting human thumb motion as a 2-dimensional joystick employing mechanomyographic signals. Infrared camera based systems such as Microsoft Digits and ultrasound sensor based systems such as Chirp Microsystems' Chirp gesture recognizers are elegant solutions, but have line-of-sight sensing limitations. Here, we present a low-cost and wearable joystick designed as a wristband which captures muscle sounds, also called mechanomyographic signals. The interface learns from user's thumb gestures and finally interprets these motions as one of the four kinds of thumb movements. We obtained an overall classification accuracy of 81.5% for all motions and 90.5% on a modified metric. Results obtained from the user study indicate that mechanomyography based wearable thumb-joystick is a feasible design idea worthy of further study. / Master of Science

The goal of this project was to determine the feasibility of non-invasively monitoring the blood gases of construction workers for carbon monoxide exposure via pulse oximetry. In particular, this study sought to understand the impact of motion artifacts caused by the worker's activities and to determine if those activities would prevent the blood gas sensor from detecting the onset of carbon monoxide poisoning. This feasibility study was conducted using a blood oxygen sensor rather than a blood carbon monoxide sensor for several reasons. First, blood gas sensors that measure blood carbon monoxide are not readily available in suitable physical form factors. Second, sensors for blood oxygen and blood carbon monoxide operate on the same physical principles and thus will be affected in the same way by worker motions. Finally, using a blood oxygen sensor allowed the study to be conducted without exposing the human subjects to carbon monoxide. A user study was conducted to determine the distribution of motion artifacts that would be created during a typical work day. By comparing that distribution to a worst-case estimate of time to impairment, the probability that helmet will adequately monitor the worker can be established. The results of the study show that the helmet will provide a measurement capable of warning the user of on setting carbon monoxide poisoning with a probability greater than 99%. / Master of Science

In this thesis, an activity classification algorithm is developed to support a human ambulatory monitoring system. This algorithm, to be deployed on an e-textile garment, represents the enabling step in creating a wide range of garments that can use the same classifier without having to re-train for different sensor types. This flexible operation is made possible by basing the classifier on an abstract model of the human body that is the same across all sensor types and subject bodies. In order to support low power devices inherent for wearable systems, the algorithm utilizes regular expressions along with a tree search during classification. To validate the approach, a user study was conducted using video motion capture to record subjects performing a variety of activities. The subjects were randomly placed into two groups, one used to generate the activities known by the classifier and another to be used as observation to the classifier. These two sets were used to gain insight on the performance of the algorithm. The results of the study demonstrate that the algorithm can successfully classify observations, so as long as precautions are taken to prevent the activities known by the classifier to become too large. It is also shown that the tree search performed by the classification can be utilized to partially classify observations that would otherwise be rejected by the classifier. The user study additionally included subjects that performed activities purely used for observations to the classifier. With this set of recordings, it was demonstrated that the classifier does not over-fit and is capable of halting the classification of an observation. / Master of Science

This thesis measured the mental workload associated with operating a voice activated software application run on wearable computer under five different communication styles (buttons, command line, icon buttons, icon text menus, and text menus). The goal of this thesis was to determine which communication style would be best allow wearable computer users to simultaneously perform other non-computer tasks. Thirty subjects were randomly assigned to using one of five software versions (n = 6), each of which utilized a unique communication style. The mental workload associated with operating each version was assessed by monitoring the performance of secondary tasks. Secondary tasks consisted of completing a block assembly, digit subtraction, and walking along a marked pathway. Each secondary task was performed twice by itself and once while operating one of the software versions, creating a total of nine trials per subject. Block assembly task performance measures included average assembly time, percentage correct blocks, and percentage correct blocks attempted. Digit subtraction measures included percentage of correct digits. And path walking measures included average walking speed. Subjective estimates of mental workload were also collected for those trials in which subjects operated the wearable computer and performed physical tasks using the NASA Task Load Index (TLX). Finally, usability information was collected for each software version via a questionnaire form. Each of the five versions of the experimental software application was operationally identical to the others, but utilized a separate communication style. The button version displayed available functions via sets of labeled buttons in the control screen. The icon button version replaced the appearance of these buttons with labeled icons. The text menu version displayed available functions textually via a pull down main menu. The icon text version displayed appended icons to the left of each main menu item. Finally, the command line version displayed no labels, buttons, menus, or icons for any functions. The experimental software was designed as a day planner/scheduling application used to set reminder dates on a calendar, edit task lists, and edit phone listings. Under the multiple resource view of mental workload, it was hypothesized that the different versions and secondary tasks would demand distinct types of mental resource and, consequently, that mental workload would be observed as lowest when the version and secondary task demanded different types of mental resources. In contrast, it was also hypothesized that mental workload would be observed as highest when the version and secondary task demanded the same type of mental resources. Although separate one way ANOVAs performed on all secondary task measures failed to indicate statistically significant differences in mental workload across the versions, secondary task performance was consistently observed as best for subjects using the icon button version. Analysis of NASA TLX subscale data indicated that the block assembly task was rated as requiring less effort and the digit task rated as requiring less mental demand when the icon button version was used. These results generally support using an icon button communication style for wearable computer software applications. Results of this study are applicable to the design of the user interface of wearable computers. These results not only report subjective and objective measures for assessing the amount of mental effort associated with operating a wearable computer and performing various physical tasks simultaneously, but also provide estimates for determining the amount of physical task performance decrement to expect when wearable computer are also operated. Such data may be used to determine human factors guidelines for matching wearable computer interfaces to physical tasks so that interference between the two is minimal. / Master of Science

Wearable computers and e-textiles are increasingly prevalent in today's society. Motion capture is one of many potential applications for on-body electronic systems. Self-contained motion capture applications require information from sensors distributed throughout the body on a "smart" garment. Therefore, this thesis presents the design of a flexible hardware platform for e-textile motion capture applications. This thesis also presents software for one such application, namely, tracking the pose or relative position of body limbs. The accuracy of this solution is compared to an industrial optical motion capture system. The combined hardware and software design are successful at collecting and processing motion capture data in the context of an e-textile jumpsuit. / Master of Science

Electrical components constantly being scaled down in size allows for small, inexpensive sensors to be placed on or around the human body for motion sensing applications. In addition, the merging of textiles with electrical components, known as electronic textiles (e-textiles), allows for these sensors to be placed directly on a wearable fabric. Simulation allows for extensive application testing and verification before prototype development. This thesis presents a simulation environment for motion sensing E-textiles. Specifically, this environment incorporates motion capture position data to simulate a rate sensing gyroscope and a dual-axis accelerometer. In addition, this simulation environment is applied to the field of gait analysis, which is the process of quantification and interpretation of a person's stride, to calculate a subject's step length. / Master of Science