Fusion of wearable and visual sensors for human motion analysis

Human motion analysis is concerned with the study of human activity recognition, human motion tracking, and the analysis of human biomechanics. Human motion analysis has applications within areas of entertainment, sports, and healthcare. For example, activity recognition, which aims to understand and identify different tasks from motion can be applied to create records of staff activity in the operating theatre at a hospital; motion tracking is already employed in some games to provide an improved user interaction experience and can be used to study how medical staff interact in the operating theatre; and human biomechanics, which is the study of the structure and function of the human body, can be used to better understand athlete performance, pathologies in certain patients, and assess the surgical skill of medical staff. As health services strive to improve the quality of patient care and meet the growing demands required to care for expanding populations around the world, solutions that can improve patient care, diagnosis of pathology, and the monitoring and training of medical staff are necessary. Surgical workflow analysis, for example, aims to assess and optimise surgical protocols in the operating theatre by evaluating the tasks that staff perform and measurable outcomes. Human motion analysis methods can be used to quantify the activities and performance of staff for surgical workflow analysis; however, a number of challenges must be overcome before routine motion capture of staff in an operating theatre becomes feasible. Current commercial human motion capture technologies have demonstrated that they are capable of acquiring human movement with sub-centimetre accuracy; however, the complicated setup procedures, size, and embodiment of current systems make them cumbersome and unsuited for routine deployment within an operating theatre. Recent advances in pervasive sensing have resulted in camera systems that can detect and analyse human motion, and small wearable sensors that can measure a variety of parameters from the human body, such as heart rate, fatigue, balance, and motion. The work in this thesis investigates different methods that enable human motion to be more easily, reliably, and accurately captured through ambient and wearable sensor technologies to address some of the main challenges that have limited the use of motion capture technologies in certain areas of study. Sensor embodiment and accuracy of activity recognition is one of the challenges that affect the adoption of wearable devices for monitoring human activity. Using a single inertial sensor, which iii captures the movement of the subject, a variety of motion characteristics can be measured. For patients, wearable inertial sensors can be used in long-term activity monitoring to better understand the condition of the patient and potentially identify deviations from normal activity. For medical staff, inertial sensors can be used to capture tasks being performed for automated workflow analysis, which is useful for staff training, optimisation of existing processes, and early indications of complications within clinical procedures. Feature extraction and classification methods are introduced in thesis that demonstrate motion classification accuracies of over 90% for five different classes of walking motion using a single ear-worn sensor. To capture human body posture, current capture systems generally require a large number of sensors or reflective reference markers to be worn on the body, which presents a challenge for many applications, such as monitoring human motion in the operating theatre, as they may restrict natural movements and make setup complex and time consuming. To address this, a method is proposed, which uses a regression method to estimate motion using a subset of fewer wearable inertial sensors. This method is demonstrated using three sensors on the upper body and is shown to achieve mean estimation accuracies as low as 1.6cm, 1.1cm, and 1.4cm for the hand, elbow, and shoulders, respectively, when compared with the gold standard optical motion capture system. Using a subset of three sensors, mean errors for hand position reach 15.5cm. Unlike human motion capture systems that rely on vision and reflective reference point markers, commonly known as marker-based optical motion capture, wearable inertial sensors are prone to inaccuracies resulting from an accumulation of inaccurate measurements, which becomes increasingly prevalent over time. Two methods are introduced in this thesis, which aim to solve this challenge using visual rectification of the assumed state of the subject. Using a ceiling-mounted camera, a human detection and human motion tracking method is introduced to improve the average mean accuracy of tracking to within 5.8cm in a laboratory of 3m × 5m. To improve the accuracy of capturing the position of body parts and posture for human biomechanics, a camera is also utilised to track the body part movements and provide visual rectification of human pose estimates from inertial sensing. For most subjects, deviations of less than 10% from the ground truth are achieved for hand positions, which exhibit the greatest error, and the occurrence of sources of other common visual and inertial estimation errors, such as measurement noise, visual occlusion, and sensor calibration are shown to be reduced.