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The gold standards for gait analysis are instrumented walkways and marker-based motion capture systems, which require costly infrastructure and are only available in hospitals and specialized gait clinics. Even though the completeness and the accuracy of these systems are unquestionable, a mobile and pervasive gait analysis alternative suitable for non-hospital settings is a clinical necessity. Using inertial sensors for gait analysis has been well explored in the literature with promising results. However, the majority of the existing work does not consider realistic conditions where data collection and sensor placement imperfections are imminent. Moreover, some of the underlying assumptions of the existing work are not compatible with pathological gait, decreasing the accuracy. To overcome these challenges, we propose a foot-mounted inertial sensor-based gait analysis system that extends the well-established zero-velocity update and Kalman filtering methodology. Our system copes with various cases of data collection difficulties and relaxes some of the assumptions invalid for pathological gait (e.g., the assumption of observing a heel strike during a gait cycle). The system is able to extract a rich set of standard gait metrics, including stride length, cadence, cycle time, stance time, swing time, stance ratio, speed, maximum/minimum clearance and turning rate. We validated the spatio-temporal accuracy of the proposed system by comparing the stride length and swing time output with an IR depth-camera-based reference system on a dataset comprised of 22 subjects. Furthermore, to highlight the clinical applicability of the system, we present a clinical discussion of the extracted metrics on a disjoint dataset of 17 subjects with various neurological conditions.

Inertial measurement units (IMUs) are commonly used for localization or movement tracking in pervasive healthcare-related studies, and gait analysis is one of the most often studied topics using IMUs. The increasing variety of commercially available IMU devices offers convenience by combining the sensor modalities and simplifies the data collection procedures. However, selecting the most suitable IMU device for a certain use case is increasingly challenging. In this study, guidelines for IMU selection are proposed. In particular, seven IMUs were compared in terms of their specifications, data collection procedures, and raw data quality. Data collected from the IMUs were then analyzed by a gait analysis algorithm. The difference in accuracy of the calculated gait parameters between the IMUs could be used to retrace the issues in raw data, such as acceleration range or sensor calibration. Based on our algorithm, we were able to identify the best-suited IMUs for our needs. This study provides an overview of how to select the IMUs based on the area of study with concrete examples, and gives insights into the features of seven commercial IMUs using real data.

Human activity recognition and behavior monitoring in a home setting using wireless sensor networks (WSNs) provide a great potential for ambient assisted living (AAL) applications, ranging from health and wellbeing monitoring to resource consumption monitoring. However, due to the limitations of the sensor devices, challenges in wireless communication and the challenges in processing large amounts of sensor data in order to recognize complex human activities, WSN-based AAL systems are not effectively integrated in the home environment. Additionally, given the variety of sensor types and activities, selecting the most suitable set of sensors in the deployment is an important task. In order to investigate and propose solutions to such challenges, we introduce a WSN-based multimodal AAL system compatible for homes with multiple residents. Particularly, we focus on the details of the system architecture, including the challenges of sensor selection, deployment, networking and data collection and provide guidelines for the design and deployment of an effective AAL system. We also present the details of the field study we conducted, using the systems deployed in two different real home environments with multiple residents. With these systems, we are able to collect ambient sensor data from multiple homes. This data can be used to assess the wellbeing of the residents and identify deviations from everyday routines, which may be indicators of health problems. Finally, in order to elaborate on the possible applications of the proposed AAL system and to exemplify directions for processing the collected data, we provide the results of several human activity inference experiments, along with examples on how such results could be interpreted. We believe that the experiences shared in this work will contribute towards accelerating the acceptance of WSN-based AAL systems in the home setting.

Inertial measurement units (IMUs) enable easy to operate and low-cost data recording for gait analysis. When combined with treadmill walking, a large number of steps can be collected in a controlled environment without the need of a dedicated gait analysis laboratory. In order to evaluate existing and novel IMU-based gait analysis algorithms for treadmill walking, a reference dataset that includes IMU data as well as reliable ground truth measurements for multiple participants and walking speeds is needed. This article provides a reference dataset consisting of 15 healthy young adults who walked on a treadmill at three different speeds. Data were acquired using seven IMUs placed on the lower body, two different reference systems (Zebris FDMT-HQ and OptoGait), and two RGB cameras. Additionally, in order to validate an existing IMU-based gait analysis algorithm using the dataset, an adaptable modular data analysis pipeline was built. Our results show agreement between the pressure-sensitive Zebris and the photoelectric OptoGait system (r = 0.99), demonstrating the quality of our reference data. As a use case, the performance of an algorithm originally designed for overground walking was tested on treadmill data using the data pipeline. The accuracy of stride length and stride time estimations was comparable to that reported in other studies with overground data, indicating that the algorithm is equally applicable to treadmill data. The Python source code of the data pipeline is publicly available, and the dataset will be provided by the authors upon request, enabling future evaluations of IMU gait analysis algorithms without the need of recording new data.

Analysis of human behaviour for deducing health and well-being information is one of the contemporary challenges given the ageing in place. To this end, existing and newly developed machine learning methods are needed to be evaluated using annotated real-world data sets. However, the metrics used in performance evaluation are directly taken from the machine learning domain, and they do not necessarily consider the specific needs of human behaviour analysis such as recognizing the duration, start time and frequency of the activities. Moreover, the commonly used metrics such as accuracy or F -measure can be misleading in the presence of skewed class distributions as in the case of human behaviour recognition. In this study, we evaluate the performance of two machine learning methods, hidden Markov model and time windowed neural network on five different real-world data sets through human behaviour understanding for health assessment perspective. According to the experimental results, standard metrics fail to reveal the actual performance of the two compared machine learning methods in terms of behaviour recognition. On the other hand, the proposed evaluation mechanism which considers three different activity categories leads to a more realistic evaluation of the overall performance.

Urban citizens, who are at the forefront of the existence of the city, in the changing socio-economic structure of the modern age, in the open and green areas of the urban areas, which are the social reinforcement areas of the cities they live in, in order to get away from the stress and distress in their daily lives with the transportation activity they use in the realization of their activities such as working, shopping, education and health. they relax spiritually and physically, socialize by establishing social relations and move away from the chaos of the city. Parks as an active usage area offered by urban open and green spaces to the citizens; The changes in the quality of the parks are effective on the basis of the perception that people leave as a result of the various social, cultural and physical activity opportunities offered to the citizens and the maintenance and management of these spatial arrangements. In addition to the historical and cultural accumulation of the Fatih District in the Historic Peninsula selected within the scope of the research, the spatial transformation of the existing urban profile shaped by the political, cultural, economic and internal-external migration effects observed recently has been observed. In order to examine the differences in the quality of the spaces created in the park areas, the quality criteria determined as a result of the literature surveys were tried to be put forward through professional observation and survey studies. In order to test the hypotheses created for the determination of public space quality criteria, 319 participants using the park areas selected from 5 different points were asked 35 questions regarding the demographic and outdoor quality criteria by considering the socio-economic and park usage variations and functional differences of the district. The results of the survey were evaluated with frequency analysis and chi-square significance tests in SPSS program. As a result of the study, it was seen that the satisfaction and reactions of the users changed according to the physical infrastructure and location of the parks and it was effective on demographic differences and general quality criteria. It has been found that the quality criteria standards are related to the perception of quality parking of the users. With the spatial observation study conducted during the research, the results of the user surveys are largely consistent with each other.

In the past few years, CMOS image sensors has performed an enormous growth in technology and their market is broadened with the integration cameras on the cell phones. The advancement trend continues as the pixel sizes getting smaller and the array formats getting larger. With pixels decreasing in size and growing in numbers, faster row read-out speed requirements have emerged to keep frame rates constant. Column parallel ADC architectures meet these demands as they utilize large numbers of parallel conversion channels.This thesis presents the design of a 12-bit column parallel Two-Step Multi-Slope (TSMS) analog to digital converter for low power CMOS image sensors. TSMS ADC architecture enables larger conversion speeds compared to widely implemented Single Slope architecture on its Two-Step Single-Slope (TSSS) mode. Proposed design can achieve even larger readout speeds in the TSMS mode, where it exploits the relaxed quantization noise requirements for larger shot noise, introduced to the circuitry by vi pixels subject to a large photon flux, by reducing the conversion resolution and increasing the conversion speed.The design is realized for pixel pitch of 6.7µm. Power consumption per column ADC is 88 µW and sampling speeds larger than 50kS/s is supported. The prototype IC generates timing and biasing signals on its own. Using SPI interface, bias voltages can be trimmed with the help of DACs and timing signals can be programmed to adapt different operation modes and speeds. Layout of the design is drawn using 180nm process and 3.15 mm × 3.15 mm sized prototype IC is sent to multi-project-wafer MPW run for fabrication.

Small suppliers often face challenges to obtain financing for their operations. Especially in developing economies, traditional financing methods can be very costly or unavailable to such suppliers. To reduce channel costs, large buyers have recently begun implementing their own financing methods that intermediate between suppliers and financing institutions. In this paper, we analyze the role and efficiency of buyer intermediation in supplier financing. Building a game-theoretical model, we show that buyer intermediated financing can significantly improve channel performance, and can simultaneously benefit both supply chain participants. Using data from a large Chinese online retailer and through structural regression estimation, we demonstrate that buyer intermediation lowers interest rates and wholesale prices, increases order fill rates, and boosts supplier borrowing. Based on counterfactual analysis on the data, we predict that the implementation of buyer intermediated financing will improve channel profits by 13.05%, increasing supplier and retailer profits by more than 10% each, and yielding approximately $44 million projected savings for the retailer. The online supplement is available at https://doi.org/10.1287/mnsc.2017.2863 . This paper was accepted by Vishal Gaur, operations management.

Background We evaluated anxiety, pain, and oral-health-related quality of life in individuals treated with conventional fixed appliances (Group A) and clear aligners (Group B) for moderate malocclusion during the initial phase of orthodontic treatment. Methods Sixty individuals, separated into Group A ( n  = 30) and Group B ( n  = 30), were included in the study. They completed the Anxiety Levels, Oral Health Impact Profile-14, and Oral Health Related Quality of Life - United Kingdom/Surveys after the application of attachments on days 0 (T1), 10 (T10), and 20 (T20). Their pain levels were evaluated with the Visual Analogue Scale on days 0, 2, and 6 in the 2nd and 6th hours and on the 1st, 3rd, 7th, 14th, and 21st days. Results Per the VAS questionnaire, pain levels in the 2nd hour, 6th hour, 1st day, and 3rd day were significantly lower in Group B than in Group A. In the OHIP-14 survey results, the comparison between Group A and Group B showed a significant difference only on the 1st day. The STAI and OHRQoL-UK survey results did not differ significantly between the groups. Conclusions We found no significant difference between the two groups in terms of anxiety levels, and pain among individuals in Group A was higher than in Group B only at the beginning of the treatment. No significant differences were observed in terms of individuals’ quality of life. Trial registration NCT06133296 (retrospectively registered)- Registration Date:15/11/2023.

Evapotranspiration (ET) is a vital process involving the transfer of water from the Earth's surface to the atmosphere through soil evaporation and plant transpiration. Accurate estimation of ET is important for a variety of applications, including irrigation management and water resource planning. The two-source energy balance (TSEB) model is a commonly used method for estimating ET using remotely sensed data. This study used the TSEB model and high-resolution unmanned aerial vehicle (UAV) imagery to estimate sorghum ET under four different irrigation regimes over two growing seasons in 2020 and 2021. The study also validated net radiation (Rn) flux through hand-held radiometer measurements and compared the estimated ET with a soil water balance model. The study outcomes revealed that that the TSEB model capably estimated Rn values, aligning well with ground-based Rn measurements for all irrigation treatments (RMSE = 32.9–39.8 W m−2 and MAE = 28.1–35.2 W m−2). However, the TSEB model demonstrated robust performance in estimating ET for fully irrigated conditions (S1), while its performance diminished with increasing water stress (S2, S3, and S4). The R2, RMSE, and MAE values range from 0.64 to 0.06, 10.94 to 17.04 mm, and 7.09 to 11.43 mm, respectively, across the four irrigation treatments over a 10-day span. These findings not only suggest the potential of UAVs for ET mapping at high-resolution over large areas under various water stress conditions, but also highlight the need for further research on ET estimation under water stress conditions.