A Handheld Quantifiable Soft Tissue Manipulation Device for Tracking Real-Time Dispersive Force-Motion Patterns to Characterize Manual Therapy Treatment.

OBJECTIVE: Low back pain (LBP) is one of the leading neuromusculoskeletal (NMSK) problems around the globe. Soft Tissue Manipulation (STM) is a force-based, non-invasive intervention used to clinically address NMSK pain conditions. Current STM practice standards are mostly subjective, suggesting an urgent need for quantitative metrics. This research aims at developing a handheld, portable smart medical device for tracking real-time dispersive force-motions to characterize manual therapy treatments as Quantifiable Soft Tissue Manipulation (QSTM). METHODS: The device includes two 3D load-cells to quantify compressive and planar-shear forces, coupled with a 6 degrees-of-freedom IMU sensor for acquiring volitionally adapted therapeutic motions while scanning and mobilizing myofascial restrictions over larger areas of the body. These force-motions characterize QSTM with treatment parameters (targeted force, application angle, rate, direction, motion pattern, time) as a part of post-processing on a PC software (Q-Ware©). A human case study was conducted to treat LBP as proof-of-concept for the device's clinical usability. RESULTS: External validation of treatment parameters reported adequate device precision required for clinical use. The case study findings revealed identifiable therapeutic force-motion patterns within treatments indicating subject's elevated force-endurance with self-reported pain reduction. CONCLUSION: QSTM metrics may enable study of STM dosing for optimized pain reduction and functional outcomes using documentable manual therapy. Clinical trials will further determine its reliability and comparison to conventional STM. SIGNIFICANCE: This medical device technology not only advances the state-of-the-art manual therapy with precision rehabilitation but also augments practice with reproducibility to examine neurobiological responses of individualized STM prescriptions for NMSK pathology.

Quantifiable Soft Tissue Manipulation (QSTM™) - A novel modality to improve clinical manual therapy with objective metrics.

Soft Tissue Manipulation (STM), a form of mechanotherapy, offers a clinical modality to examine and treat Neuromusculoskeletal (NMS) pain disorders and dysfunction. The, current STM practice is mostly subjective and reliant on anecdotal patient feedback and lacks quantification with objective metrics. This paper proposes Quantifiable Soft Tissue Manipulation (QSTM™), a sensor based computerized technological advancement in Soft tissue examination and treatment enabling new standard of practice in manual therapy. This novel medical device technology aims to produce optimum STM prescriptions using ergonomic, portable, handheld medical tools with specially contoured tips designed to palpate and assess tissue anomalies of specific musculoskeletal conditions. QSTM™ captures three-dimensional forces and motion of the mechatronic handheld tools to quantify STM treatment parameters, such as (resultant force, force application angle, rate, direction, and treatment time). Clinical practice using QSTM™ facilitates real-time visual feedback of treatment metrics and subsequent treatment documentation for comparison and analysis on a Windows based computer software (Q-Ware©). Pre-clinical testing using the QSTM™ medical device system clearly identifies inconsistencies among practitioners and distinguishes STM practice variabilities. Thus, QSTM™ is an apt tool for soft tissue treatment assessment, analysis, and individualized prescriptions for targeted STM dosing and commercialization.

Improving the Robustness of Object Detection Through a Multi-Camera-Based Fusion Algorithm Using Fuzzy Logic.

A single camera creates a bounding box (BB) for the detected object with certain accuracy through a convolutional neural network (CNN). However, a single RGB camera may not be able to capture the actual object within the BB even if the CNN detector accuracy is high for the object. In this research, we present a solution to this limitation through the usage of multiple cameras, projective transformation, and a fuzzy logic-based fusion. The proposed algorithm generates a "confidence score" for each frame to check the trustworthiness of the BB generated by the CNN detector. As a first step toward this solution, we created a two-camera setup to detect objects. Agricultural weed is used as objects to be detected. A CNN detector generates BB for each camera when weed is present. Then a projective transformation is used to project one camera's image plane to another camera's image plane. The intersect over union (IOU) overlap of the BB is computed when objects are detected correctly. Four different scenarios are generated based on how far the object is from the multi-camera setup, and IOU overlap is calculated for each scenario (ground truth). When objects are detected correctly and bounding boxes are at correct distance, the IOU overlap value should be close to the ground truth IOU overlap value. On the other hand, the IOU overlap value should differ if BBs are at incorrect positions. Mamdani fuzzy rules are generated using this reasoning, and three different confidence scores ("high," "ok," and "low") are given to each frame based on accuracy and position of BBs. The proposed algorithm was then tested under different conditions to check its validity. The confidence score of the proposed fuzzy system for three different scenarios supports the hypothesis that the multi-camera-based fusion algorithm improved the overall robustness of the detection system.

Time-Domain Data Fusion Using Weighted Evidence and Dempster-Shafer Combination Rule: Application in Object Classification.

To apply data fusion in time-domain based on Dempster-Shafer (DS) combination rule, an 8-step algorithm with novel entropy function is proposed. The 8-step algorithm is applied to time-domain to achieve the sequential combination of time-domain data. Simulation results showed that this method is successful in capturing the changes (dynamic behavior) in time-domain object classification. This method also showed better anti-disturbing ability and transition property compared to other methods available in the literature. As an example, a convolution neural network (CNN) is trained to classify three different types of weeds. Precision and recall from confusion matrix of the CNN are used to update basic probability assignment (BPA) which captures the classification uncertainty. Real data of classified weeds from a single sensor is used test time-domain data fusion. The proposed method is successful in filtering noise (reduce sudden changes-smoother curves) and fusing conflicting information from the video feed. Performance of the algorithm can be adjusted between robustness and fast-response using a tuning parameter which is number of time-steps( t s ).

Paradox Elimination in Dempster-Shafer Combination Rule with Novel Entropy Function: Application in Decision-Level Multi-Sensor Fusion.

Multi-sensor data fusion technology in an important tool in building decision-making applications. Modified Dempster-Shafer (DS) evidence theory can handle conflicting sensor inputs and can be applied without any prior information. As a result, DS-based information fusion is very popular in decision-making applications, but original DS theory produces counterintuitive results when combining highly conflicting evidences from multiple sensors. An effective algorithm offering fusion of highly conflicting information in spatial domain is not widely reported in the literature. In this paper, a successful fusion algorithm is proposed which addresses these limitations of the original Dempster-Shafer (DS) framework. A novel entropy function is proposed based on Shannon entropy, which is better at capturing uncertainties compared to Shannon and Deng entropy. An 8-step algorithm has been developed which can eliminate the inherent paradoxes of classical DS theory. Multiple examples are presented to show that the proposed method is effective in handling conflicting information in spatial domain. Simulation results showed that the proposed algorithm has competitive convergence rate and accuracy compared to other methods presented in the literature.

Development of an Artificial Finger-Like Knee Loading Device to Promote Bone Health.

This study presents the development of an innovative artificial finger-like device that provides position specific mechanical loads at the end of the long bone and induces mechanotransduction in bone. Bone cells such as osteoblasts are the mechanosensitive cells that regulate bone remodelling. When they receive gentle, periodic mechanical loads, new bone formation is promoted. The proposed device is an under-actuated multi-fingered artificial hand with 4 fingers, each having two phalanges. These fingers are connected by mechanical linkages and operated by a worm gearing mechanism. With the help of 3D printing technology, a prototype device was built mostly using plastic materials. The experimental validation results show that the device is capable of generating necessary forces at the desired frequencies, which are suitable for the stimulation of bone cells and the promotion of bone formation. It is recommended that the device be tested in a clinical study for confirming its safety and efficacy with patients.

A Mechatronic Loading Device to Stimulate Bone Growth via a Human Knee.

This paper presents the design of an innovative device that applies dynamic mechanical load to human knee joints. Dynamic loading is employed by applying cyclic and periodic force on a target area. The repeated force loading was considered to be an effective modality for repair and rehabilitation of long bones that are subject to ailments like fractures, osteoporosis, osteoarthritis, etc. The proposed device design builds on the knowledge gained in previous animal and mechanical studies. It employs a modified slider-crank linkage mechanism actuated by a brushless Direct Current (DC) motor and provides uniform and cyclic force. The functionality of the device was simulated in a software environment and the structural integrity was analyzed using a finite element method for the prototype construction. The device is controlled by a microcontroller that is programmed to provide the desired loading force at a predetermined frequency and for a specific duration. The device was successfully tested in various experiments for its usability and full functionality. The results reveal that the device works according to the requirements of force magnitude and operational frequency. This device is considered ready to be used for a clinical study to examine whether controlled knee-loading could be an effective regimen for treating the stated bone-related ailments.

Development of a Portable Knee Rehabilitation Device That Uses Mechanical Loading.

Joint loading is a recently developed mechanical modality, which potentially provides a therapeutic regimen to activate bone formation and prevent degradation of joint tissues. To our knowledge, however, few joint loading devices are available for clinical or point-of-care applications. Using a voice-coil actuator, we developed an electromechanical loading system appropriate for human studies and preclinical trials that should prove both safe and effective. Two specific tasks for this loading system were development of loading conditions (magnitude and frequency) suitable for humans, and provision of a convenient and portable joint loading apparatus. Desktop devices have been previously designed to evaluate the effects of various loading conditions using small and large animals. However, a portable knee loading device is more desirable from a usability point of view. In this paper, we present such a device that is designed to be portable, providing a compact, user-friendly loader. The portable device was employed to evaluate its capabilities using a human knee model. The portable device was characterized for force-pulse width modulation duty cycle and loading frequency properties. The results demonstrate that the device is capable of producing the necessary magnitude of forces at appropriate frequencies to promote the stimulation of bone growth and which can be used in clinical studies for further evaluations.