Based on the study of 34 healthy and 19 osteoarthritic knees in three different age groups (early, middle and late adulthood), this thesis reports the discovery of the potential of knee acoustic emission (AE) as a biomarker for quantitative assessment of joint ageing and degeneration. Signal processing and statistical analysis were conducted on the joint angle signals acquired using electronic goniometers attached to the lateral side of the legs during repeated sit- stand-sit movements. A four-phase movement model derived from joint angle measurement is proposed for statistical analysis, and it consists of the ascending- acceleration and ascending-deceleration phases in the sit-to- stand movement, followed by the descending-acceleration and descending-deceleration phases in the stand-to-sit movement. Through the quantitative assessment of joint angle signals based on the four-phase model established, statistical differences of different knee conditions related to age and degeneration were discovered based on cycle-by- cycle variations and movement symmetry. For AE burst signals acquired from piezo-electric sensors attached to the knee joints during repeated sit-stand-sit movements, the statistical analysis started from the quantity of AE events in the proposed four movement phases and extended to waveform features extracted from AE signals. While the quantity of AE events was found to follow certain statistical trends related to age and degeneration in each movement phase, detail statistical analysis of AE waveform features yielded the peak amplitude value and average signal level of each AE burst as two most significant features. An image based knee AE feature profile is presented based on 2D colour histograms formed by the peak amplitude value and average signal level in four movement phases. It provides not only a visual trend related to knee age and degeneration, but also enables visual assessment of the