http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/646 Digital Transformation Technology Sat, 25 Jul 2026 23:54:15 GMT 2026-07-25T23:54:15Z http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/1822 Reactive balance strategy Identification using motion capture and unsupervised clustering Werapat Lapawong Worasak Rueangsirarak Balance is maintained through the continuous integration of visual, vestibular, and somatosensory inputs, translated into coordinated joint movements across the lower limb. Traditional balance assessments rely on subjective clinical observation, which limits the ability to detect complex, multi-joint strategies and hidden postural patterns. This thesis proposes an objective, data-driven framework integrating motion capture technology with unsupervised machine learning to identify balance strategies in both adult and pediatric populations. The research was conducted in two phases. In Phase 1, the framework was developed and validated using the publicly available KINECAL adult dataset from PhysioNet. Three clustering algorithms — Agglomerative Hierarchical, Gaussian Mixture Model, and K-Means — were applied to lower-limb joint angle data, identifying three postural archetypes: Good Balance, Slight Unbalance, and Poor Balance. K-Means achieved the best performance at k=3, confirmed by the Elbow Method and Silhouette Score. Continuously, the research was scoped down to explore the hidden balance strategies in children who experienced the Chronic Ankle Instability within the past year. In Phase 2, the framework was extended to 44 children aged 7 to 13 years-old performing the m-CTSIB under four sensory conditions, using the Xsens 17-sensor IMU system, underwent rigorous normalization and multicollinearity mitigation before being processed through four clustering algorithms: K-means, Spectral, BIRCH, and Hierarchical. The optimal four-cluster solution was validated through internal clustering metrics and PCA visualizations. Our analysis successfully delineated our condition-specific postural archetypes: Mixed Strategy (Hip-Ankle), Ankle-Knee Strategy, and Atypical/Divergent patterns — each varying in joint amplitude and directional dominance across sensory conditions. Notably, the results revealed a sensory-dependent shift in motor control logic; while participants favored a localized Ankle Strategy during baseline and visual-deprivation tasks on stable surfaces, the removal of somatosensory cues forced a strategic shift toward a Mixed Strategy (Hip-Ankle). In the most demanding scenarios—where both visual and somatosensory inputs were compromised—the majority of children transitioned to a complex Integrated Strategy, necessitating synchronized Hip, Knee, and Ankle adjustments to maintain stability. Furthermore, the model also identified rare, atypical clusters characterized by divergent joint movement patterns — including opposite-directional responses and extreme distal amplitudes — that remain largely invisible to manual clinical observation. To evaluate the quality and validity of clusters, this thesis proposed the new set of input features extracted from three rotational degrees of freedom (DoF) along with their joint descriptions. Cluster interpretations were further characterized through a plane-specific kinematic analysis — decomposing joint behavior across the sagittal (flexion/extension), frontal (abduction/adduction), and transverse (internal/external rotation) planes — consistent with ISB-recommended tri-planar reporting conventions. Each feature was further processed into three proposed temporal formats which are Discrete 5-second intervals (D5I), Segmented 5-second averages (S5A), and Overall trial mean (OTM). The metrics i.e. Silhouette Coefficient (SC), Calinski-Harabasz Index (CH), and Davies-Bouldin Index (DB) were used to confirm that the best clustering algorithm to identify the balance strategies in human are vary depending on different clinical scenarios. Collectively, these findings demonstrate the testing of four different scenarios show that FiSEOTL reveals the majority of the population uses the Mixed Strategy (Hip-Ankle) by resulting of BIRCH clustering algorithm with the SC=0.420, CH=29.677, DB=0.578; while FiSECTL and FoSEOTL reveal the majority of the population use the Ankle-Knee Strategy by resulting of Spectral clustering algorithm with the SC=0.548, CH=22.759, DB=0.448 and Hierarchical Clustering with the SC=0.404, CH=28.940, DB=0.663 respectively; and the FoSECTL reveals the majority of the population uses the Mixed Strategy (Hip, Knee, Ankle) by resulting of K-Means clustering algorithm with the SC=0.416, CH=33.812, DB=0.720. Therefore, this thesis demonstrates that unsupervised clustering applied to motion capture data provides objective and clinically meaningful insights into balance strategy use that traditional observation-based assessment cannot offer. Thesis (M.Sc.) -- Digital Transformation Technology, School of Applied Digital Technology. Mae Fah Luang University, 2025 Wed, 01 Jan 2025 00:00:00 GMT http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/1822 2025-01-01T00:00:00Z http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/1788 Prototyping and evaluating LoRaWAN communication devices Nattaphit Jengsriwong Suppakarn Chansareewittaya In this research, presents the development and apply a DIY LoRaWAN Gateway operating in the 925MHz frequency band. LoRaWAN technology is renowned for its long-range communication capabilities and low power usage. Currently, it is utilized by only certain groups of users and is not as widespread as Wi-Fi due to the high prices of gateways in the market. Consequently, existing gateways are tailored for devices with reasonable costs. Therefore, we propose designing a cost-effective, moderately sized, customizable, and high-quality gateway that significantly reduces costs. In this development, utilize Raspberry Pi as the main processing unit and integrate it with LoRaHAT for Pi. We configure the gateway to enable seamless communication between the gateway, EndNode, and NetworkServer, and ensure flexibility in modifying component formats to suit various project requirements. The goal of this project is to make LoRaWAN technology accessible through efficient, cost-effective solutions that can be self-built and cater to diverse target groups, such as individual users, makers, and smart farm owners. The aim is to facilitate widespread adoption and promote innovation in IoT usage. Thesis (M.Sc.) -- Digital Transformation Technology, School of Applied Digital Technology. Mae Fah Luang University, 2025 Wed, 01 Jan 2025 00:00:00 GMT http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/1788 2025-01-01T00:00:00Z http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/1786 A Study of the effectiveness of a forklift driving simulation: A comparison between virtual reality controllers and mobile-based controllers Natkitta Kongkaewvorasit Suppakarn Chansareewittaya Forklift operation training carries inherent safety risks and high costs. To address this, this study developed a simulation system integrating a miniature forklift with IoT and Virtual Reality (VR) within a Digital Twin framework. The research compared operational effectiveness and user satisfaction between VR controllers and mobile-based controllers. Drawing from a continuous research framework (Protocol Number: EC 25067-13), participants performed navigation and pallet handling tasks using a learning-by-doing approach. The findings indicate that while both modalities facilitate conceptual understanding, the VR-based interaction demonstrated notably higher engagement and situational awareness due to its immersive nature. The study concludes that VR-based simulation effectively enhances procedural skills and provides a safe, engaging platform for technical and vocational education. Thesis (M.Sc.) -- Digital Transformation Technology, School of Applied Digital Technology. Mae Fah Luang University, 2025 Wed, 01 Jan 2025 00:00:00 GMT http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/1786 2025-01-01T00:00:00Z http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/1755 Prototyping of intelligent medication platform Areeelak Sukkaew Suppakarn Chansareewittaya Currently, medication non-adherence remains a significant problem, particularly among elderly patients and individuals with chronic diseases. Forgetfulness and limited access to healthcare services often lead to missed doses, resulting in reduced treatment effectiveness and increased health risks. To address these challenges, this research presents the design, development, and technical evaluation of the Intelligent Medication Platform. The Intelligent Medication Platform is an IoT-based medication management system constructed with an acrylic structure and powered by a Raspberry Pi 4 as the central controller. The system integrates medication reminders, an electric door-lock dispensing mechanism, real-time video conferencing, and activity logging through a web-based interface. Key hardware components include a relay-controlled magnetic lock, speaker, monitor, camera module, and database system. The system evaluation was designed to emphasize technical performance validation rather than user satisfaction assessment. To ensure comprehensive performance verification, three principal experimental procedures were conducted. First, command execution accuracy was evaluated through 100 consecutive operational cycles for each core system function. Second, power consumption analysis was performed under various operating conditions to determine energy efficiency and operational requirements. Third, 72-hours continuous operation (stress) test was carried out to assess system stability, reliability, and long-term operational endurance. The results demonstrate high functional reliability, with 98% accuracy in door unlocking, 100% reliability in alarm notification, 96% success rate in video call connectivity, and over 97% accuracy in database logging. The system maintained stable operation with acceptable energy consumption and 100% uptime during the continuous testing period. These findings confirm that the Intelligent Medication Platform is technically reliable, energy-efficient, and suitable for home healthcare deployment to support medication adherence and telemedicine services. Independent Study (M.Sc.) -- Digital Transformation Technology, School of Applied Digital Technology. Mae Fah Luang University, 2025 Wed, 01 Jan 2025 00:00:00 GMT http://mfuir.mfu.ac.th:80/xmlui/handle/123456789/1755 2025-01-01T00:00:00Z