Automatic Functional Shoulder Task Identification and Sub-task Segmentation Using Wearable Inertial Measurement Units for Frozen Shoulder Assessment

Smartwatch-based functional assessment for upper extremity impairment after musculoskeletal injuries: A pilot study

Introduction

Wearable sensors are increasingly applied to rehabilitation for arm movement analysis. However, simple and clinically relevant applications are scarce.

Objectives

To investigate the feasibility of single smart watch-based parameters for functional assessment in upper limb rehabilitation for musculoskeletal injuries using a commercial smart watch.

Method

Ten patients with unilateral shoulder pain and range-of-motion limitations were enrolled. They wore Galaxy Watch® and performed three sets of upper extremity tasks consisting of gross activities-of-daily-living tasks, Wolf Motor Function Test (WMFT), and Upper Extremity Functional Index (UEFI), and the acceleration and angular velocities were acquired. The motion segment size (MSS), representing motion smoothness from a clinical perspective, and various sensor-based parameters were extracted. The correlation between the parameters and clinical outcome measures were analyzed. The percent relative range (PRR) of the significant parameters was also calculated.

Results

For overhead and behind body activity task set, mean MSS for elbow flexion/extension axis significantly correlated with WMFT score (R = 0.784, p = .012). For planar tasks, mean MSS for the forearm supination/pronation (R = 0.815, p = .007) and shoulder rotation (R = 0.870, p = .002) axes significantly correlated with WMFT score. For forearm and fine movement task set, mean MSS of the elbow flexion/extension angle showed significant correlation with WMFT (R = 0.880, p < .001) and UEFI (R = 0.718, p = .019). The total performance time (R = -0.741, p = .014) also showed significant correlation with WMFT score. The PRR for mean MSS in forearm supination (71.5%, planar tasks) and mean MSS in x-direction (49.8%, forearm and fine motor movements) were similar to the PRR of WMFT (58.5%), suggesting sufficient variation range across different degree of impairments.

Conclusion

The commercial smart watch-based parameters showed consistent potential for use in clinical functional assessments.

Multi-Task Learning U-Net for Functional Shoulder Sub-Task Segmentation

The assessment of a frozen shoulder (FS) is critical for evaluating outcomes and medical treatment. Analysis of functional shoulder sub-tasks provides more crucial information, but current manual labeling methods are time-consuming and prone to errors. To address this challenge, we propose a deep multi-task learning (MTL) U-Net to provide an automatic and reliable functional shoulder sub-task segmentation (STS) tool for clinical evaluation in FS. The proposed approach contains the main task of STS and the auxiliary task of transition point detection (TPD). For the main STS task, a U-Net architecture including an encoder-decoder with skip connection is presented to perform shoulder sub-task classification for each time point. The auxiliary TPD task uses lightweight convolutional neural networks architecture to detect the boundary between shoulder sub-tasks. A shared structure is implemented between two tasks and their objective functions of them are optimized jointly. The fine-grained transition-related information from the auxiliary TPD task is expected to help the main STS task better detect boundaries between functional shoulder sub-tasks. We conduct the experiments using wearable inertial measurement units to record 815 shoulder task sequences collected from 20 healthy subjects and 43 patients with FS. The experimental results present that the deep MTL U-Net can achieve superior performance compared to using single-task models. It shows the effectiveness of the proposed method for functional shoulder STS. The code has been made publicly available at https://github.com/RobinChu9890/MTL-U-Net-for-Functional-Shoulder-STS.Clinical Relevance- This work provides an automatic and reliable functional shoulder sub-task segmentation tool for clinical evaluation in frozen shoulder.

IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review

With the rapid development of Internet of Things (IoT) technologies, traditional disease diagnoses carried out in medical institutions can now be performed remotely at home or even ambient environments, yielding the concept of the Internet of Health Things (IoHT). Among the diverse IoHT applications, inertial measurement unit (IMU)-based systems play a significant role in the detection of diseases in many fields, such as neurological, musculoskeletal, and mental. However, traditional numerical interpretation methods have proven to be challenging to provide satisfying detection accuracies owing to the low quality of raw data, especially under strong electromagnetic interference (EMI). To address this issue, in recent years, machine learning (ML)-based techniques have been proposed to smartly map IMU-captured data on disease detection and progress. After a decade of development, the combination of IMUs and ML algorithms for assistive disease diagnosis has become a hot topic, with an increasing number of studies reported yearly. A systematic search was conducted in four databases covering the aforementioned topic for articles published in the past six years. Eighty-one articles were included and discussed concerning two aspects: different ML techniques and application scenarios. This review yielded the conclusion that, with the help of ML technology, IMUs can serve as a crucial element in disease diagnosis, severity assessment, characteristic estimation, and monitoring during the rehabilitation process. Furthermore, it summarizes the state-of-the-art, analyzes challenges, and provides foreseeable future trends for developing IMU-ML systems for IoHT.

Multiphase Identification Algorithm for Fall Recording Systems Using a Single Wearable Inertial Sensor

Fall-related information can help clinical professionals make diagnoses and plan fall prevention strategies. The information includes various characteristics of different fall phases, such as falling time and landing responses. To provide the information of different phases, this pilot study proposes an automatic multiphase identification algorithm for phase-aware fall recording systems. Seven young adults are recruited to perform the fall experiment. One inertial sensor is worn on the waist to collect the data of body movement, and a total of 525 trials are collected. The proposed multiphase identification algorithm combines machine learning techniques and fragment modification algorithm to identify pre-fall, free-fall, impact, resting and recovery phases in a fall process. Five machine learning techniques, including support vector machine, k-nearest neighbor (kNN), naïve Bayesian, decision tree and adaptive boosting, are applied to identify five phases. Fragment modification algorithm uses the rules to detect the fragment whose results are different from the neighbors. The proposed multiphase identification algorithm using the kNN technique achieves the best performance in 82.17% sensitivity, 85.74% precision, 73.51% Jaccard coefficient, and 90.28% accuracy. The results show that the proposed algorithm has the potential to provide automatic fine-grained fall information for clinical measurement and assessment.