1 ABSTRACT Title: Comparing physiotherapist's functional test assessments with OpenPose kinematic data Objectives: Assess wether is OpenPose (OP) system able to detect parameters of poor performance in the Single Leg Squat Test and Step Down Test. Presence of these parameters was indicated by consensus process among 3 physiotherapists. Methods: 48 healthy volunteers participated on the research. Each volunteer demonstrated 3 repetitions of Single Leg Squat Test and Step Down Test on each leg. Repetitions were recorded on video and at the same time evaluated by 3 physiotherapists (PT) using 15 binary parameters. Consensus process indicating presence of a parameter involved agreement of at least 2 of the raters while the inter-rater reliability coeficient for all 3 raters had to be κ ≥ 0,41. Changes in angles and relative distances of observed body segments were calculated using OpenPose keypoint detection model. Therefore each execution of the functional test was described by OP data and PT data. Statistical analysis was performed to assess wether was OpenPose able to detect significant differences between poor and good execution of a functional test in the 15 rated parameters. Results: OP was able to reliably assess following parameters: Loss of balance, Excessive upper extremity movement and Lateral...

Title: Evaluation of the range of motion of the upper limbs using the OpenPose program Objectives: The aim of the work was to determine whether it is possible to use the OpenPose program as a tool for measuring upper limb range of motion, highlight OpenPose limits and consider whether the program could be used for evaluation in clinical practise or as part of telerehabiliation in the future. Methods: 48 probands participated in the research. Participants were intentionally selected from healthy population due to the nature of the work. Each participant was measured in 6 different positions while lying on a lounger. Measurements were taken bilaterally. A total of 14 different angles of shoulder and elbow were measured. These were maximum flexion of the elbow joint, flexion of the arm with flexion of the elbow, flexion of the arm, internal and external rotation of the arm and extension of the arm. Data were collected using a standardised examination method using a goniometer and a video recording. Video recordings were later used in the OpenPose program for angle evaluation. Finally, the results of both methods were compared. Results: 8 out of total 14 measured angles of shoulder and elbow were excluded based on evaluation criteria. Values with average difference between both examiner's results...

The waste of building energy consumption is a major challenge in the world. And the real-time detection of human thermal comfort is an effective way to meet this issue. As mentioned in name, it means to detect the human’s comfort level in real-time and non-invasively. However, due to the various factors such as individual difference of thermal comfort, elements related to climatic (temperature, humidity, illumination, etc.) and so on, there is still a long way to implement this strategy in real life. From another perspective, the current HVAC (heating, ventilating and air-conditioning) systems cannot provide flexible interaction channels to adjust atmosphere, and naturally fails to satisfy requirements of users. All of them indicate the necessity to develop a detection method for human thermal comfort. In this paper, a non-invasion detection method toward human thermal comfort is proposed from two perspectives: macro human postures and skin textures. In posture part, OpenPose is used for analyzing the position coordinates of human body key points’ in images, for example, elbow, knee, and hipbone, etc. And the results of analyzing would be interpreted from the term of thermal comfort. In skin textures, deep neural network is used to predict the temperature of human skins via images. Based on Fanger’s theory of thermal comfort, the results of both parts are satisfying: subjects’ postures can be captured and interpreted into different thermal comfort level: hot, cold and comfort. And the absolute error of prediction from neurons network is less than 0.125 degrees centigrade which is the equipment error of thermometer used in data acquisition. With the solution proposed by this paper, it is promising to non-invasively detect the thermal comfort level of users from postures and skin textures. Finally, theconclusion and future work are discussed in final chapter. / Slöseriet med att bygga energiförbrukningen är en stor utmaning i världen. Ochdetektering av mänsklig termisk komfort i realtid är ett effektivt sätt att lösaproblemet. Som nämns i namn betyder det att detektera människans komfortnivå i realtid och icke-invasivt. På grund av de olika faktorerna som individuell skillnad i termisk komfort, är emellertid faktorer som är relaterade till klimat (temperatur, luftfuktighet, belysning etc.) det fortfarande en lång väg att implementera denna strategi i verkligheten. Från ett annat perspektiv kan nuvarande system för uppvärmning, ventilation och luftkonditionering inte tillhandahålla flexibla interaktionskanaler för att anpassa atmosfären och naturligtvis misslyckas till nöjda krav från användarna. Alla indikerar nödvändigheten av att utveckla en detekteringsmetod för mänsklig termisk komfort. I detta dokument föreslås en ickeinvasion detekteringsmetod mot mänsklig termisk komfort från två perspektiv: makro mänskliga hållningar och hudtexturer. I hållningspartiet används OpenPose för att analysera positionskoordinaterna för kroppens huvudpunkter i bilder, till exempel armbåge, knä och höftben osv. Och resultaten av analysen skulle tolkas från termen av termisk komfort. I hudtexturer används djupt neuralt nätverk för att förutse temperaturen på mänskliga skinn via bilder. Baserat på Fangers teorin om värmekomfort är resultaten av båda delarna tillfredsställande: subjektens hållningar kan fångas och tolkas till olika värmekomfortnivåer: varm, kall och komfort. Och det absoluta felet av prediktering från neuronnätverket är mindre än 0,125 grader Celsius, vilket är utrustningsfelet hos termometern som används vid datainsamling. Med lösningar i detta papper är det lovande att detektera användarens värmekomfortnivå fritt från invändningar och hudtexturer. Slutligen diskuteras slutsatserna och detframtida arbetet i sista kapitlet.

The aim of this scoping review is to investigate current applications of a markerless human pose estimation (HPE) algorithm in sports and exercise science. 17 studies are selected for this pur-pose. Results show that HPE is applied already in a variety of sports for different aims and pur-poses. Even though it provides many advantages over marker-based approaches, it still comes with challenges that need to be tackled in future research. / Ziel dieser Übersichtsarbeit ist es, die aktuellen Anwendungen eines markerlosen Algorithmus zur Schätzung der menschlichen Körperhaltung (HPE) in der Sport- und Bewegungswissenschaft zu untersuchen. Zu diesem Zweck wurden 17 Studien ausgewählt. Die Ergebnisse zeigen, dass HPE bereits in einer Vielzahl von Sportarten mit unterschiedlichen Zielen und Zwecken eingesetzt wird. Obwohl sie viele Vorteile gegenüber markerbasierten Ansätzen bietet, gibt es immer noch Herausforderungen, die in der zukünftigen Forschung angegangen werden müssen.