Capturing Upper Body Kinematics and Localization with Low-Cost Sensors for Rehabilitation Applications

Sarker, Anik; Emenonye, Don-Roberts; Kelliher, Aisling; Rikakis, Thanassis; Buehrer, R. Michael; Asbeck, Alan T.

doi:10.3390/s22062300

Cited by 9 publications

(4 citation statements)

References 90 publications

(122 reference statements)

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“…Some data for the ADL layer is already capturable through questionnaires as well as simple wearables (like activity monitoring applications on a smartphone). IMU based tracking of the affected limb, hand, and torso using methods being developed by our lab [34] and other research [8] can significantly increase observability of the daily activity. We are integrating unobtrusive IMUs units in the SARAH system [34].…”

Section: Discussionmentioning

confidence: 99%

“…IMU based tracking of the affected limb, hand, and torso using methods being developed by our lab [34] and other research [8] can significantly increase observability of the daily activity. We are integrating unobtrusive IMUs units in the SARAH system [34]. The IMUs will be worn on the two wrists, index finger of the affected limb and on the waist throughout the day, including the training sessions at the home.…”

Section: Discussionmentioning

confidence: 99%

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A Hierarchical Bayesian Model for Cyber-Human Assessment of Rehabilitation Movement

Ahmed

Thopali

Rikakis

et al. 2022

Preprint

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Background: The evidence-based quantification of the relation between changes in movement quality and functionality can assist clinicians in achieving more effective structuring or adaptations of therapy. Facilitating this quantification through computational tools can also result in the generation of large-scale data sets that can inform automated assessment of rehabilitation. Interpretable automated assessment can leave more time for clinicians to focus on treatment and allow for remotely supervised therapy at the home. Methods: In our first experiment, we developed a rating process and accompanying computational tool to assist clinicians in following a standardized movement assessment process relating functionality to movement quality. We conducted three studies with three different versions of the computational rating tool. Clinicians rated task, segment, and movement feature performance for 440 videos in which stroke survivors executed standardized upper extremity therapy tasks related to functional activities. In our second experiment, we used the 440 rated videos, in addition to 140 videos of unimpaired subjects performing the same tasks, to improve our previously developed automated assessment ensemble model that automatically generates segmentation times and task ratings across the impaired and unimpaired movement. The automated assessment ensemble integrates expert knowledge constraints into data-driven training through a combination of HMM, transformer, MSTCN++, and decision tree computational modules. In our third experiment, we used the therapist and automated ratings to develop a four-layer Hierarchical Bayesian Model (HBM) for computing the statistical relation of movement quality changes to functionality. We first calculated conditional layer probabilities using clinician ratings of task, segment, and movement features. We increased the granularity of observation of the HBM by formulating ΔHBM, a correlation graph between kinematics and movement composite features. Finally, we used k-means clustering on the ΔHBM to identify three clusters of features among the 16 movement composite and 20 kinematic features and used the centroid of these clusters as the weights of the input data to our computational assessment ensemble. Results: We evaluated the efficacy of our rating interface in terms of inter-rater reliability (IRR) across tasks, segments, and movement features. The third version of the interface produced an average IRR of 67%, while the time per session (TPS) was the lowest of the three studies. By analyzing the ratings, we were able to identify a small number of movement features that have the highest probability of predicting functional improvement. We evaluated the performance of our automated assessment model using 60% impaired and 40% unimpaired movement data and achieved a frame-wise segmentation accuracy of 87.85±0.58 and a block-segmentation accuracy of 98.46±1.6. We also demonstrated the performance of our proposed HBM in correlation to clinician ratings with a correlation of over 90%. The HBM also generates a correlation graph, ΔHBM that relates 16 composite movement features to the 20 kinematic features. We can thus integrate the HBM into the computational assessment ensemble to perform automated and integrated movement quality and functionality assessment that is driven by computationally extracted kinematics. Conclusions: Combining standardized clinician ratings of videos with knowledge based and data-driven computational analysis of rehabilitation movement allows the expression of an HBM that increases the observability of the relation of movement quality to functionality and enables the training of computational algorithms for automated assessment of rehabilitation movement. While our work primarily focuses on the upper extremity of stroke survivors, the models can be adopted to many other neurorehabilitation contexts.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A Hierarchical Bayesian Model for Cyber-Human Assessment of Rehabilitation Movement

Ahmed

Thopali

Rikakis

et al. 2022

Preprint

Self Cite

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“…The growing interest in telemedicine and patient monitoring has led to a rapid rise in the use of wearable technologies in healthcare systems [ 12 ]. However, a practical approach to monitoring physical rehabilitation or evaluating the physical capacity of a patient at home can be challenging because it is difficult to capture meaningful data in a low-cost and easy-to-use way [ 13 ]. The monitoring system must be minimally burdensome for the patient, in order to obtain natural movement data and to ensure high patient acceptance levels regarding the technology.…”

Section: Introductionmentioning

confidence: 99%

Validation of 3D Knee Kinematics during Gait on Treadmill with an Instrumented Knee Brace

Reneaud

Zory

Guérin

et al. 2023

Sensors

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To test a novel instrumented knee brace intended for use as a rehabilitation system, based on inertial measurement units (IMU) to monitor home-based exercises, the device was compared to the gold standard of motion analysis. The purpose was to validate a new calibration method through functional tasks and assessed the value of adding magnetometers for motion analysis. Thirteen healthy young adults performed a 60-second gait test at a comfortable walking speed on a treadmill. Knee kinematics were captured simultaneously, using the instrumented knee brace and an optoelectronic camera system (OCS). The intraclass correlation coefficient (ICC) showed excellent reliability for the three axes of rotation with and without magnetometers, with values ranging between 0.900 and 0.972. Pearson’s r coefficient showed good to excellent correlation for the three axes, with the root mean square error (RMSE) under 3° with the IMUs and slightly higher with the magnetometers. The instrumented knee brace obtained certain clinical parameters, as did the OCS. The instrumented knee brace seems to be a valid tool to assess ambulatory knee kinematics, with an RMSE of <3°, which is sufficient for clinical interpretations. Indeed, this portable system can obtain certain clinical parameters just as well as the gold standard of motion analysis. However, the addition of magnetometers showed no significant advantage in terms of enhancing accuracy.

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“…For this purpose, various flexible wearable sensors have been developed to monitor the above vital signals. For example, flexible wearable strain sensors are attached to the human body to monitor joint movement information, and inertial sensors are used together to improve training performances [ 5 ] and warn injury [ 6 ] during movement. Flexible wearable sensors can monitor the cardiovascular vital signs continuously and in real-time, including ECG [ 7 , 8 ], heart rate [ 9 , 10 ], blood pressure [ 11 ], blood oxygen [ 12 ], and blood glucose [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

A Review of Recent Advances in Vital Signals Monitoring of Sports and Health via Flexible Wearable Sensors

Sun

Guo

Yang

et al. 2022

Sensors

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In recent years, vital signals monitoring in sports and health have been considered the research focus in the field of wearable sensing technologies. Typical signals include bioelectrical signals, biophysical signals, and biochemical signals, which have applications in the fields of athletic training, medical diagnosis and prevention, and rehabilitation. In particular, since the COVID-19 pandemic, there has been a dramatic increase in real-time interest in personal health. This has created an urgent need for flexible, wearable, portable, and real-time monitoring sensors to remotely monitor these signals in response to health management. To this end, the paper reviews recent advances in flexible wearable sensors for monitoring vital signals in sports and health. More precisely, emerging wearable devices and systems for health and exercise-related vital signals (e.g., ECG, EEG, EMG, inertia, body movements, heart rate, blood, sweat, and interstitial fluid) are reviewed first. Then, the paper creatively presents multidimensional and multimodal wearable sensors and systems. The paper also summarizes the current challenges and limitations and future directions of wearable sensors for vital typical signal detection. Through the review, the paper finds that these signals can be effectively monitored and used for health management (e.g., disease prediction) thanks to advanced manufacturing, flexible electronics, IoT, and artificial intelligence algorithms; however, wearable sensors and systems with multidimensional and multimodal are more compliant.

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Capturing Upper Body Kinematics and Localization with Low-Cost Sensors for Rehabilitation Applications

Cited by 9 publications

References 90 publications

A Hierarchical Bayesian Model for Cyber-Human Assessment of Rehabilitation Movement

A Hierarchical Bayesian Model for Cyber-Human Assessment of Rehabilitation Movement

Validation of 3D Knee Kinematics during Gait on Treadmill with an Instrumented Knee Brace

A Review of Recent Advances in Vital Signals Monitoring of Sports and Health via Flexible Wearable Sensors

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