Patient recovery after total knee arthroplasty remains highly variable. Despite the growing interest in and implementation of patient reported outcome measures (e.g. Knee Society Score, Oxford Knee Score), the recovery process of the individual patient is poorly monitored. Unfortunately, patient reported outcomes represent a complex interaction of multiple physiological and psychological aspects, they are also limited by the discrete time intervals at which they are administered. The use of wearable sensors presents a potential alternative by continuously monitoring a patient's physical activity. These sensors however present their own challenges. This paper deals with the interpretation of the high frequency time signals acquired when using accelerometer-based wearable sensors. During a preliminary validation, five healthy subjects were equipped with two wireless inertial measurement units (IMUs). Using adhesive tape, these IMU sensors were attached to the thigh and shank respectively. All subjects performed a series of supervised activities of daily living (ADL) in their everyday environment (1: walking, 2: stair ascent, 3: stair descent, 4: sitting, 5: laying, 6: standing). The supervisor timestamped the performed activities, such that the raw IMU signals could be uniquely linked to the performed activities. Subsequently, the acquired signals were reduced in Python. Each five second time window was characterized by the minimum, maximum and mean acceleration per sensor node. In addition, the frequency response was analyzed per sensor node as well as the correlation between both sensor nodes. Various machine learning approaches were subsequently implemented to predict the performed activities. Thereby, 60% of the acquired signals were used to train the mathematical models. These models were than used to predict the activity associated with the remaining 40% of the experimentally obtained data.Introduction & Aims
Method
Over the last decade, sensor technology has proven its benefits in total knee arthroplasty, allowing the quantitative assessment of tension in the medial and lateral compartment of the tibiofemoral joint through the range of motion (VERASENSE, OrthoSensor Inc, FL, USA). In reversal total shoulder arthroplasty, it is well understood that stability is primarily controlled by the active and passive structures surrounding the articulating surfaces. At current, assessing the tension in these stabilizing structures remains however highly subjective and relies on the surgeons’ feel and experience. In an attempt to quantify this feel and address instability as a dominant cause for revision surgery, this paper introduces an intra-articular load sensor for reverse total shoulder arthroplasty (RTSA). Using the capacitive load sensing technology embedded in instrumented tibial trays, a wireless, instrumented humeral trial has been developed. The wireless communication enables real-time display of the three-dimensional load vector and load magnitude in the glenohumeral joint during component trialing in RTSA. In an in-vitro setting, this sensor was used in two reverse total shoulder arthroplasties. The resulting load vectors were captured through the range of motion while the joint was artificially tightened by adding shims to the humeral tray.Introduction & Aims
Method
For nearly 58% of total knee arthroplasty (TKA) revisions, the reason for revision is exacerbated by component malalignment. Proper TKA component alignment is critical to functional outcomes/device longevity. Several methods exist for orthopedic surgeons to validate their cuts, however, each has its limitations. This study developed/validated an accurate, low-cost, easy to implement first-principles method for calculating 2D (sagittal/frontal plane) tibial tray orientation using a triaxial gyroscope rigidly affixed to the tibial plateau of a simulated leg jig and validated 2D tibial tray orientation in a human cadaveric model. An initial simulation assessed error in the sagittal/frontal planes associated with all geometric assumptions over a range of positions (±10°, ±10°, and −3°/0°/+3° in the sagittal, frontal, and transverse planes, respectively). Benchtop experiments (total positions - TP, clinically relevant repeated measures - RM, novice user - NU) were completed using a triaxial gyroscope rigidly affixed to and aligned with the tibial tray of the fully adjustable leg-simulation jig. Finally, two human cadaveric experiments were completed. A similar triaxial gyroscope was mounted to the tibial tray of a fresh frozen human cadaver to validate sagittal and frontal plane tibial tray orientation. In cadaveric experiment one, three unique frontal plane shims were utilized to measure changes in frontal plane angle. In cadaveric experiment two, measurements using the proprosed gyroscopic method were compared with computer navigation at a series of positions. For all experiments, one rotation of the leg was completed and gyroscopic data was processed through a custom analysis algorithm.Introduction
Methods
