Abstract
Introduction & Aims
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.
Method
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.
Results
An overview of the obtained prediction accuracy per model stratified by ADL is provided in Table 1. The Nearest Neighbor and Random Forest algorithms performed worse compared to the Support Vector Machine and Decision Tree approaches. Even for the latter, differentiating between walking and stair ascent/descent remains challenging as well as differentiating between sitting, standing and laying. The prediction accuracies are however exceeding 90% for all activities when using the Support Vector Machine approach. This is further illustrated in Figure 1, indicating the actual versus predicted activity for the validation set.
Conclusions
In conclusion, this paper presents an evaluation of different machine learning algorithms for the classification of activities of daily living from accelerometer-based wearable sensors. This facilitates evaluating a patient's ability to walk, climb or descend stairs, stand, lay or sit on a daily basis, understanding how active the patient is overall and which activities are routinely performed following arthroplasty surgery. Currently, effort is undertaken to understand how participation in these activities progresses with recovery following total knee arthroplasty.
