Total knee arthroplasty (TKA) is the most common treatment for degenerative osteoarthritis of that articulation. However, either in rehabilitation clinics or in hospital wards, the knee range of motion (ROM) can currently only be assessed using a goniometer. In order to provide continuous and objective measurements of knee ROM, we propose the use of wearable inertial sensors to record the knee ROM during the recovery progress. Digitalized and objective data can assist the surgeons to control the recovery status and flexibly adjust rehabilitation programs during the early acute inpatient stage. The more knee flexion ROM regained during the early inpatient period, the better the long-term knee recovery will be and the sooner early discharge can be achieved. The results of this work show that the proposed wearable sensor approach can provide an alternative for continuous monitoring and objective assessment of knee ROM recovery progress for TKA patients compared to the traditional goniometer measurements.

Falls are the major risks and threats among elderly population. Various studies have developed automatic critical fall detection systems for emergency alarms and medical services. Window sizes and segmentation approaches would affect the system performance in terms of power consumption, computational speed and reliability that are essential considerations in the real-world implementation. Intuitively, employing shorter window sizes and simpler segmentation approaches leads to a faster detection speed and better power efficiency. However, few works explore the effects of windowing methods on detection performance, especially for machine learning models. This paper investigates the impact of segmentation approaches and window sizes in wearable-based critical fall detection systems with machine learning models. Two generally used segmentation approaches, including sliding windows and impact-defined windows, are explored with a range of window sizes from 0.5 s to 5.0 s and four machine learning models: Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Classification and Regression Tree (CART) and Naove Bayes (NB). The results show that sliding windows are more sensitive to the changes of window sizes compared to impact-defined windows. However, the differences between the best performance of both segmentation approaches are within 1% (99.66% in sliding windows and 99.78% in impact-defined windows). For systems with SVM and k-NN models, using 0.5 s window sizes in sliding windows and combinations of 1.5 s backward and forward windows in impact-defined windows are sufficient to achieve at least 94% accuracy and 98% accuracy.

Total knee arthroplasty (TKA) is one of the most common treatments for people with severe knee osteoarthritis (OA). The accuracy of outcome measurements and quantitative assessments for perioperative TKA is an important issue in clinical practice. Timed up and go (TUG) tests have been validated to measure basic mobility and balance capabilities. A TUG test contains a series of subtasks, including sit-to-stand, walking-out, turning, walking-in, turning around, and stand-to-sit tasks. Detailed information about subtasks is essential to aid clinical professionals and physiotherapists in making assessment decisions. The main objective of this study is to design and develop a subtask segmentation approach using machine-learning models and knowledge-based postprocessing during the TUG test for perioperative TKA. The experiment recruited 26 patients with severe knee OA (11 patients with bilateral TKA planned and 15 patients with unilateral TKA planned). A series of signal-processing mechanisms and pattern recognition approaches involving machine learning-based multi-classifiers, fragmentation modification and subtask inference are designed and developed to tackle technical challenges in typical classification algorithms, including motion variability, fragmentation and ambiguity. The experimental results reveal that the accuracy of the proposed subtask segmentation approach using the AdaBoost technique with a window size of 128 samples is 92%, which is an improvement of at least 15% compared to that of the typical subtask segmentation approach using machine-learning models only.

Fluid intake is important for people to maintain body fluid homeostasis. Inadequate fluid intake leads to negative health consequences, such as headache, dizziness and urolithiasis. However, people in busy lifestyles usually forget to drink sufficient water and neglect the importance of fluid intake. Fluid intake management is important to assist people in adopting individual drinking behaviors. This work aims to propose a fluid intake monitoring system with a wearable inertial sensor using a hierarchical approach to detect drinking activities, recognize sip gestures and estimate fluid intake amount. Additionally, container-dependent amount estimation models are developed due to the influence of containers on fluid intake amount. The proposed fluid intake monitoring system could achieve 94.42% accuracy, 90.17% sensitivity, and 40.11% mean absolute percentage error (MAPE) for drinking detection, gesture spotting and amount estimation, respectively. Particularly, MAPE of amount estimation is improved approximately 10% compared to the typical approaches. The results have demonstrated the feasibility and the effectiveness of the proposed fluid intake monitoring system.

Insufficient fluid intake is becoming an actual concern and problem of the elderly with dramatic increase of aging population. Poor fluid intake habits and Dehydration can cause long-term negative effects in mental and physical capabilities, and even death. Typical approaches to monitor individual fluid intake behavior and consumption of fluid quantities mainly rely on self-report, such as questionnaires. However, these typical approaches suffer issues in manual errors and cause additional burdens on the user. In recent years, advantages of wireless sensor networks and pervasive computing technologies have provided the opportunities to create automatic, objective and low-intrusive fluid intake monitoring systems. The aim of this work is to design a 3D-printed smart cup attached with a single accelerometer and drinking event detection algorithm that is feasible to spot fluid intake gestures, especially for detecting drinking events and recognizing complete periods of drinking. Such drinking information about timing and the frequency of fluid intake can reveal fluid intake habits and that is important for clinical assessment and intervention. The finding shows that the best system performance using k-nearest neighbors (k-NN) model with window size of 4s achieves 89.92% and 85.88% F-measure in event-defined and frame-defined evaluation approaches, respectively.

The interior space of large-scale buildings, such as hospitals, with a variety of departments, is so complicated that people may easily lose their way while visiting. Difficulties in wayfinding can cause stress, anxiety, frustration and safety issues to patients and families. An indoor navigation system including route planning and localization is utilized to guide people from one place to another. The localization of moving subjects is a critical-function component in an indoor navigation system. Pedestrian dead reckoning (PDR) is a technology that is widely employed for localization due to the advantage of being independent of infrastructure. To improve the accuracy of the localization system, combining different technologies is one of the solutions. In this study, a multi-sensor fusion approach is proposed to improve the accuracy of the PDR system by utilizing a light sensor, Bluetooth and map information. These simple mechanisms are applied to deal with the issue of accumulative error by identifying edge and sub-edge information from both Bluetooth and the light sensor. Overall, the accumulative error of the proposed multi-sensor fusion approach is below 65 cm in different cases of light arrangement. Compared to inertial sensor-based PDR system, the proposed multi-sensor fusion approach can improve 90% of the localization accuracy in an environment with an appropriate density of ceiling-mounted lamps. The results demonstrate that the proposed approach can improve the localization accuracy by utilizing multi-sensor data and fulfill the feasibility requirements of localization in an indoor navigation system.