2019 MU theses - Access restricted to MU
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Item Factors affecting participation in Indonesian social forestry(University of Missouri--Columbia, 2019) Kusumawardani, Dorin Lida; McCann, Laura[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI--COLUMBIA AT REQUEST OF AUTHOR.] Social forestry was designed as a solution for massive deforestation, lack of land access and poverty issues in community dependent forests. The Indonesian Government allocated 12.7 million ha of forest area as community managed forests through a social forestry program. This paper analyzes the factors affecting household participation in Indonesian Social Forestry. Participants are defined as households who are formal members and actively participate in a social forestry group. A survey was conducted with 240 households in three different villages in the region of Kapuas Hulu, West Kalimantan. The logistic regression estimates indicate that training in forestry, more landownership, experiences of natural resource conflict and dependence on forest resources increase the likelihood of household participation in a social forestry group. The study finds that a higher education level by the head of household decreased the odds of household participation by 81 [percent]. In addition, households from Penepian Raya village were less likely to participate than households from Selaup Village.Item Four-limb IMU sensors for canine gait analysis(University of Missouri--Columbia, 2019) Zhang, Xiqiao; Duan, Dongsheng; Yao, Gang[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI--COLUMBIA AT REQUEST OF AUTHOR.] Canine models of human musculoskeletal diseases, such as Duchenne muscular dystrophy (DMD), are important translational animal models for developing therapies. Musculoskeletal diseases alter gait performance, and gait analysis could provide useful data for diagnosis and evaluating treatment outcomes. Wireless inertial measurement unit (IMU) offers low cost, 3D motion tracking and can be a viable option for musculoskeletal assessment in the canine model. This thesis research aims to develop a four limb IMU sensor system for canine gait analysis. We designed and constructed a four limb sensor mounting system using 3D printing. Custom algorithms were developed to analyze the IMU data and determine stride phase timing. To determine the accuracy of our method, we conducted synchronized gait tests with the GAIT4Dog, a commercially available system. Our algorithm detected the swing start event in both front and hind limbs with high accuracy with a mean error [plus-minus] standard deviation of 0.005 [plus-minus] 0.013 sec and 0.002 [plus-minus] 0.015 sec respectively. The system was then used to compare gait in 3 year old dogs including 7 dystrophic dogs, 3 healthy dogs, and 2 dystrophic dogs treated with gene therapy. We identified a distinct gait feature in dystrophic dogs' front limb linked to their lateral sway motion. This feature was improved in dystrophic dogs that received gene therapy. This novel four limb IMU system could serve as an effective canine gait analysis tool for veterinary applications or translational, preclinical studies in dogs for human musculoskeletal diseases.Item Investigation of the effects of body type on accelerometer based fall detection(University of Missouri--Columbia, 2019) Kurkowski, Samantha; Skubic, Marjorie[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI--COLUMBIA AT REQUEST OF AUTHOR.] Precision or personalized medicine follows the idea that analyzing a patient's genome can help stratify patients into treatment groups. Patients with cancer, for example, are split into groups based on the genetic make-up, rather than the location, of their tumor and given medicine that corresponds to their specified treatment group. Instead of prescribing the same medicine for all patients with lung cancer, doctors are able to personalize treatment based on a person's genetics. Could fall detection algorithms benefit from this type of personalized approach? This project analyzes accelerometer data containing falls and activities of daily living gathered from 16 stunt actors and 20 elderly volunteers as well as 22 falls from the FARSEEING dataset with the goal of discovering whether algorithms personalized to a person's physiological features (such as height or weight) perform better than the traditional generalized approach. This thesis demonstrates that personalized algorithms can potentially make fall detection more efficient for some people. The unscripted test results show that the algorithms personalized to build and weight, decreased false alarms in several participants as compared to the overall algorithm. The FARSEEING data results show that algorithms, personalized to weight and sex, may increase the efficiency and accuracy of fall detection algorithms. An analysis of which physiological factors help determine whether a person needs a personalized or a general algorithm is needed to determine whether or not personalized algorithms make economic sense.Item Investigation of the effects of body type on accelerometer based fall detection(University of Missouri--Columbia, 2019) Kurkowski, Samantha; Skubic, Marjorie[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI--COLUMBIA AT REQUEST OF AUTHOR.] Precision or personalized medicine follows the idea that analyzing a patient's genome can help stratify patients into treatment groups [1] [2]. Patients with cancer, for example, are split into groups based on the genetic make-up, rather than the location, of their tumor and given medicine that corresponds to their specified treatment group [3]. Instead of prescribing the same medicine for all patients with lung cancer, doctors are able to personalize treatment based on a person's genetics. Could fall detection algorithms benefit from this type of personalized approach? This project analyzes accelerometer data containing falls and activities of daily living gathered from 16 stunt actors and 20 elderly volunteers as well as 22 falls from the FARSEEING dataset with the goal of discovering whether algorithms personalized to a person's physiological features (such as height or weight) perform better than the traditional generalized approach. This thesis demonstrates that personalized algorithms can potentially make fall detection more efficient for some people. The unscripted test results show that the algorithms personalized to build and weight, decreased false alarms in several participants as compared to the overall algorithm. The FARSEEING data results show that algorithms, personalized to weight and sex, may increase the efficiency and accuracy of fall detection algorithms. An analysis of which physiological factors help determine whether a person needs a personalized or a general algorithm is needed to determine whether or not personalized algorithms make economic sense.Item Simulator study of ridesharing pickup and dropoff area design and evaluation of benefits and applications of tethered surveillance drones (TSDS)(University of Missouri--Columbia, 2019) Kaltenbronn, Jacob; Sun, Carlos[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI--COLUMBIA AT REQUEST OF AUTHOR.] Technology is reshaping transportation. An example is mobile apps that enable the growing ridesharing industry. When combined with another technology, autonomous vehicles, ridesharing is expected to become a dominant mode of transportation. To facilitate this, dedicated pickup/dropoff areas for ridesharing need to be implemented. Two pickup/dropoff area designs, one with angled parking stalls and one a curbside design, were developed and tested using pedestrian and wheelchair simulators to study the performance of the designs with respect to safety, efficiency, and accessibility from the pedestrian perspective. While both designs performed comparably in terms of safety, the curb design resulted in a shorter vehicle waiting time, indicating greater efficiency. However, results indicated that the stall design was more accessible, leading to the conclusion that both designs are viable compared to each other, with neither being clearly superior. Additionally, signage and education were found to be effective in a simulator in increasing the safety and efficiency of the pickup/dropoff areas. Another example of technological advancements is the use of drones in transportation. Drone use is increasing in limited applications, but drawbacks such as communications concerns, short flight times, and regulations limit further use. One way to overcome these limitations is implementing tethered surveillance drones (TSDs): drones connected to a powered base station via a cable tether. This enables unlimited flight times, secure communications, and the possibility for loosened regulations. These benefits could expand drone use for applications including traffic incident management, work zone monitoring, and other forms of data collection or traffic monitoring.