The joint and muscle forces arising from and generated in the knee during the activity of squatting, and rising from a deep squat have been calculated. The analysis involved the consideration of a two-dimensional model. Data was then collected from each of the subjects performing the activity using: a force platform; a ciné film used in conjunction with the X-rays to describe accurately the configuration of the lower limb; EMG data; anthropometric data. A computer program was developed to analyse the data and compute the forces in the leg. Six subjects were tested and graphs of joint and muscle forces versus knee angle were obtained for each of them. A discussion follows. The results for ascent and descent, and slow and fast activities are compared.

Apparatus capable of objectively evaluating the laxity of the knee in vivo has been developed. The equipment consisted of a microcomputer-controlled machine, into which the leg was firmly clamped. The mechanical properties of the knee were measured by slowly applying a load to the tibia, while the femur was held stationary, and monitoring the resulting displacement of the tibia. Three separate tests could be performed: anterior-posterior drawing, varus-valgus rotation and tibial rotation. The tests were carried out on both legs of each subject, making six tests in all. The forces versus displacement (or torque versus rotation) took the form of a hysteresis loop. From these a total of 24 variables describing the stiffness, laxity and visco-elastic properties of the knee were calculated.

Equipment capable of objective knee analysis has been used to obtain data from 85 'normal' healthy knees, 47 patients suffering with knee disorders, and three cadaveric knee joints. Among the 'normals' it was found that there was a correlation between body weight and stiffness and laxity. A lower stiffness and higher laxity was recorded at 20 degrees of knee flexion than at 90 degrees. Using relative-paired difference analysis the variables affected by different injuries in patients were identified and are presented. In a separate analysis a multi-variate technique is used to interpret the data. The technique could be used to predict or diagnose knee injury, and as such may be highly useful to clinicians.