This thesis explores the feasibility of developing an affordable and easy-to-use feed- back system that leverages information from multiple inertial measurement units (IMUs) to identify mistakes during upper body bodyweight training and provide real-time feedback to the user. To develop the system, a human-centered approach was used, which involved conducting semi-structured interviews with movement ex- perts and a workshop with targeted end-users to understand their needs. The study also involved 12 volunteers who performed push-ups, tricep dips, and planks cor- rectly and then made specific mistakes intentionally while wearing five IMUs. Binary and multiclass classifiers were developed to classify the exercise technique. The re- sults showed that personalized multiclass classifiers produced good to excellent quality results, while global classification techniques performed poorly. Additionally, an increased number of sensors did not always lead to improved classification re- sults, and the placement of the sensors could have a significant impact. The user in- put and evaluation of the feedback system pinpointed the need for customization and accessibility in the design. This thesis contributes to the development of a feedback system that can help users identify mistakes in their upper body bodyweight exer- cises and improve their form and technique. / Denna avhandling utforskar genomförbarheten att utveckla ett prisvärt och lättan- vänt feedbacksystem som utnyttjar information från flera inertiella mätenheter (IMU: er) för att identifiera misstag under överkroppsövningar med egen kroppsvikt och ge användaren realtidsfeedback. För att utveckla systemet användes en human- centrerad metod, som innefattade semistrukturerade intervjuer med rörelseexperter och en workshop med målinriktade slutanvändare för att förstå deras behov. Studien inkluderade också 12 frivilliga som utförde armhävningar, tricep dips och plankor korrekt och sedan medvetet gjorde specifika misstag medan de bar på fem IMU: er. Binära och flerklassklassificerare utvecklades för att klassificera övningstekniken. Resultaten visade att personliga flerklassklassificerare producerade bra till utmärkta resultat, medan globala klassificeringstekniker presterade dåligt. Dessutom ledde ett ökat antal sensorer inte alltid till förbättrade klassificeringsresultat, och placeringen av sensorerna kunde ha en betydande påverkan. Användarinput och utvärdering av feedbacksystemet pekade på behovet av anpassning och tillgänglighet i designen. Denna avhandling bidrar till utvecklingen av ett feedbacksystem som kan hjälpa an- vändare att identifiera misstag i sina överkroppsövningar med egen kroppsvikt och förbättra sin form och teknik.

Can we reduce disturbance, swing, and errors by delegating some of the tasks to a subsystem on the tool hanging from a drone to gain precision and dependability? This project covers developing and implementing a user-controlled prototype with an automatic control system and gripper for functionality and controlled movement in 5 DOF. AirForestry is developing a new way of working in the forestry business to provide a greener and healthier solution for the soil and the environment. The forestry industry emits approximately one million tons of CO2 per year from heavy diesel-fueled vehicles. With a renewable energy battery-powered drone of a diameter of 6.2 m and a hanging harvesting tool, they can significantly improve the environmental impact of forestry. The harvesting tool has an automatic control system and is user controlled to perform the thinning and cutting of trees. The main focus is to use IMU sensor data to produce accurate angle estimation to control the stability of a scaled prototype and to implement physical restrictions to make it able to be attached safely and tested on a drone but not limit the controlled mobility of the prototype with a system independent of the control system of the drone itself. The user can control the attitude and elevation and receive real-time sensor data wireless during operation. With the intended spacing of the attachment points on the drone, it can prevent unwanted swing from a push or displaced drop. It uses the accelerometer on the IMUsensor to calculate the angle of roll and pitch, although the accelerometer is sensitive to vibrations and rotations. The control system has a fast response and rise time, but it experiences noise and oscillations. Sensor fusion of the accelerometer and gyroscope in a complementary filter can be implemented to increase the accuracy of the angle estimations and decrease the noise, which will be reflected in the speed of the servos and thereby improve the stability and mobility of the system.

No description available.

No description available.

No description available.

No description available.

<p dir="ltr">Parkinson’s Disease (PD) is the second most common neurodegenerative disorder with tremor being its primary motor symptom. Although the MS-UPDRS is the current clinical method for evaluating the severity of tremors in PD, it has several drawbacks resulting from the subjective, visual-based examination, and the ordinal scale used to rate the tremors. Since, the MS-UPDRS is agnostic to the etiology of the tremor, age related increase in naturally occurring physiological tremors may confound the precise rating of PD tremors. However, replacing the judgment of the neurologist in determining the holistic progression of PD and treatment protocol is neither feasible nor advisable. This research used lightweight, wearable, non-invasive sensors to detect, analyze, and differentiate changes in wrist kinematics due to physiological and PD tremors. Findings reveal key differences and similarities in composition between these different types of tremors. Dominant frequency analysis using a data-based approach shows interesting parallels with the frequency range found in literature for these tremors. Finally, using features of tremor signal obtained from the sensors, a novel Tremor Severity Score rating scale was created that shows greater sensitivity in differentiating rest and postural tremors as well as medication effects on these tremors in PD patients compared to the MS-UPDRS. This study offers a simple method for objectively evaluating Parkinsonian tremors, identifying kinematic distinctions between rest and postural tremors, analyzing the effect of anti-parkinsonian medication on these tremors, and sensitively scoring tremors. These objective methods could be valuable for early diagnosis and distinguishing between different tremor causes in both clinical and telehealth settings, as well as for investigating the effects of various treatment methods on tremors.</p>

This work aims to introduce a methodology for the virtual assessment of inertial measurement unit (IMU)-based Body-Attached Sensor Networks (BASNs) for gesture recognition. This design framework consists of three main parts: (i) multibody modelling of the human upper body, (ii) simulation of human gestures along with data acquisition from modelled IMUs, and (iii) determination of the optimal number of sensors to include in the network along with their placements on the body. By using simulation, the boundary conditions of BASNs can be assessed in a short time without the need for costly and time-consuming user studies.

近年來穿戴相關發展越來越蓬勃，特別是在虛擬-實境的綜合藝術表 演中，例如: 電影「猩球崛起」。然而大部分虛擬實境的綜合內容是 基於腳本預先錄製好的，而且演員需要大量的練習，使表演能夠完美 演出。此外，如果我們想要在兩人的相對方向定位上有特殊效果，那 麼預先錄製的的方法是不合適的。解決這個問題的一個方法是，使用 高品質的相機偵測身體的姿勢或位置。但是精准度常會受限於光線或 是障礙物。 本篇論文中，我們提出一個即時相對方向定位方法，這方法使用無線 可穿戴式設備解決這個問題。我們結合BLE 所發送的Received Signal Strength Indicator (RSSI)與IMU 感測器資訊，來追蹤兩個表演者的相對方向定位的位置。但是RSSI 資料有波動與不穩定性、IMU 會引起 累積的誤差。我們發明了「可靠程度」的RSSI 量測概念，並且把這 概念運用在IMU 定期校正上。我們實驗的情況是，兩個人的舞蹈來 驗證準確性，結果是令人滿意的。我們還使用Unity 來實踐人體骨架， 以便與兩個舞者動作做比較。在未來，我們開發的方案可以用於藝術 表演，使內容更豐富，更具互動性。 / In recent years, wearable-related applications are flourishing, especially in virtual-real integrated art performance, such as “Rise of the Planet of the Apes”. However, most of the virtual-real integrated contents are pre-recorded based on the script, and the performer needs a lot of practice to make the integration perfect. Moreover, if we want to make special effect based on the relative directional positions of two performers, the pre-recorded approach is not suitable. One way to tackle this problem is to use the high-quality camera to detect the body posture or position.But the accuracy is usually limited in light intensity or obstacles. In this thesis, we propose a real-time relative directional positioning approach using wireless wearable devices to solve this problem. We use Received Signal Strength Indicator (RSSI) of BLE, combined with IMU sensors to track two performers’ relative directional positions. The RSSI fluctuates and the IMU causes accumulated errors. We invent the concept of “reliable level” of RSSI measures to periodically correct the IMU errors. We experiment the scenario of two-person dance to validate the accuracy, and the result is satisfactory. We also use Unity to real-time render the human skeleton for comparison with the two dancers’ motion.In the future, our developed scheme can be used in the art performance to make the content richer and more interactive.

Eine Voraussetzung um einen Flugregler für Flugroboter zu realisieren, ist die Wahrnehmung der Bewegungen dieses Roboters. Diese Arbeit beschreibt einen Ansatz zur Schätzung der Bewegung eines autonomen Flugroboters unter Verwendung relativ einfacher, leichter und kostengünstiger Sensoren. Mittels eines Erweiterten Kalman Filters werden Beschleunigungssensoren, Gyroskope, ein Ultraschallsensor, sowie ein Sensor zu Messung des optischen Flusses zu einer robusten Bewegungsschätzung kombiniert. Dabei wurden die einzelnen Sensoren hinsichtlich der Eigenschaften experimentell untersucht, welche für die anschließende Erstellung des Filters relevant sind. Am Ende werden die Resultate des Filters mit den Ergebnissen einer Simulation und eines externen Tracking-Systems verglichen.