Posts Tagged Real-time

[WEB SITE] imHere Homepage – mHealth Platform for Self-management

IMHERE

Interactive Mobile Health and Rehabilitation

iMHere is an mHealth platform promoting clinician-guided self-care to patients with chronic diseases. Internet accessibility provides a secure bridge between patients’ smartphone applications and a web-based clinician portal, and successfully empowers patients to perform subjective self-care and preventative measures. The app was designed to send monitorial data to the portal and also receive output regarding self-care regimens as recommended by the attending clinician. The combination of interactive, real-time medical monitoring with patient control offers a powerful, unique solution for patients living with chronic illnesses where cognitive and physical disabilities present significant barriers to effective self-care.

Using a web-based portal, the clinician (typically a nurse coordinator, social worker, case manager, or patient advocate) could monitor patients’ compliance with regimens and indicate self-care plans to be delivered to the patient via the app, allowing the clinician to monitor a patient’s status and intervene as needed. Clinicians could use the portal to tailor a regimen or treatment plan for each and every patient (e.g. scheduled medication, wound care instructions, etc.) and the portal would consolidate the plan to the smartphone app in real time—an advancement over existing comparable health portals which cannot push data to the app. Results of clinical implementation suggest that the iMHere app was successful in delivering values for patients and in engaging them to comply with treatment. In the first 6 months of the clinical implementation, patients have been consistently using the app for self-management tasks and to follow the regimes set up by their respective clinicians. We observed that the daily usage increased significantly in the first two months (from approximately 1.3-times/day to over 3-times/day), and then plateau at around 3.5 times per day per patient. This pattern of increasing usage in the first two months and the subsequent plateau is relatively consistent across all patients. The app is currently available in Android platform with an iPhone version under development.

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[WEB SITE] Electronic Caregiver Enters Clinical Trials With G60Trauma.org

 Dec. 7, 2018

PHOENIXDec. 7, 2018 /PRNewswire/ — Virtual Caregiving is set to enter both the healthcare industry and patient home settings. In January 2019, G60 Trauma (G60Trauma.org) will begin testing Addison Care, the world’s first, comprehensive virtual caregiving system to provide real time, 24/7 patient monitoring and care.

Dr. Alicia Mangram (center) and G60 Trauma staff visit the Electronic Caregiver tower in downtown Las Cruces, New Mexico.

Addison Care provides exciting new components to an interactive voice platform to demonstrate an interactive, augmented reality feature tied to visual sensing and connected home devices. Now, not only can you have a two-way conversation with an Electronic Caregiver, but the technology comes alive with an expertly designed augmented reality character named Addison, developed on AWS Sumerian. Addison provides a breakthrough user interface.

What can Addison do? In a clinical setting, Addison can greet a patient, recognized through facial recognition, conduct a verbal health examination, collect vitals, and even direct a comprehensive gait and balance session to determine the probability of a ground level fall in a particular patient. In the residential environment, Addison provides medication reminders, verifies medication consumption, provides medical test reminders, monitors vitals, demonstrates rehabilitation exercises, assesses a patient’s progress, mood, fall risk and responses to escalating conditions and emergencies including contacting responders or caregivers in time of patient need.

How does Addison work? A network of wireless visual sensors, local AI (artificial intelligence)-based processors, interactive tablets, Bluetooth biometric devices and emergency monitoring devices will be setup in a residence. Addison Care will be marketed and supported by a network of nationwide private duty home care providers that will serve as both live caregivers and Addison Care representatives. CEO of SDS, Anthony Dohrmann said, “Our goal is to expand affordable population health careto the masses, while lightening the burden on providers and payers. We are delivering an exciting new form of technology to patients and the active aging to improve their quality of life and health outcomes.” Addison will be making its debut at the Las Vegas Consumer Electronics Show January 8-11, 2019, Booth: Sands Convention Center Halls A-D – 42142.

Why partner with G60 Trauma Organization? Dr. Alicia Mangram, founder of G60 Trauma in Phoenix, Arizona, is a surgeon and acclaimed trauma specialist who has devoted her career to improving trauma care through advocacy, surgical and critical care research, education and community services. G60 Trauma is a specialized care program designed for trauma patients over the age of 60, with the goal of optimizing their recovery and safely discharging them back to their homes. This partnership will allow us to study hundreds of patients who have had a ground level fall and provide us with the data and information we need to continue producing products and services geared toward prevention and superior outcomes.

With an expert research team of professionals behind hundreds of successful research publications and processes, G60 Trauma team will be conducting an expansive study involving over 500 patients to document the effectiveness of Addison Care and Electronic Caregiver on improving patient outcomes, increasing patient and family satisfaction, reducing hospital readmission and reducing mortalities. Also, improving treatment adherence with the hope of validating a more effective, outcome based, continuum of care capable of reducing the long-term pressures and costs associated with long-term care and chronic disease management.

“The costs of treatment non-adherence have been reported to be as high as $300B annually and is noted as being responsible for 50% of all treatment failures. In a period of nursing and physician shortages, where home care is inadequate in frequency partly due to high cost, our hope is that Addison Care and Electronic Caregiver can fill the gap in patient care and bring better outcomes to the masses,” Dr. Alicia Mangram stated.

About SameDay Security, Inc. and Electronic Caregiver

SameDay Security (SDS) is one of the fastest growing monitored technology providers in the U.S. and one of only a handful of nationwide service providers. Known as the Electronic Caregiver CompanyTM and founded in 2009, SDS currently provides automated home care solutions and safety devices nationwide to thousands of clients. SDS has invested over $35,000,000 in patient screenings, research and development. SDS will disclose a new capital offering after CES to fuel new product launches and expansion. SDS has developing contracts with hundreds of home care partners across America who will participate in Addison Care marketing to their clients. New clinical trials are scheduled with G60 Trauma of Phoenix, Arizona, involving 500 patients over 3 years to determine the impact on patient outcomes, cost reduction, lower hospitalization, chronic disease management and long-term care. Electronic Caregiver employs over 70 employees and is headquartered in Las Cruces, New Mexico. www.electroniccaregiver.com

About G60Trauma.org
G60 is a specialized trauma care program developed by Dr. Alicia Mangram. Since 2009, Dr. Mangram has devoted her career to improving trauma care through advocacy, surgical and critical care research, education and community services. In the beginning of her career, she quickly realized that a traumatic injury in patients 60 years and older could occur from a simple fall resulting in a hip fracture. The traditional approach was to admit them to a medical facility and await medical clearance for pre-existing conditions such as diabetes, heart disease, etc. prior to any surgery.

While patients waited for medical clearance, other medical related complications could develop. Recognizing the cause of these complications lead to a paradigm shift and implementing an aggressive care approach for our G60 population. Through evidence-based research, Dr. Mangram and her team developed a care plan to address the needs of G60 trauma patients. These care plans achieved several goals, such as: Expedited early identification in the ER, admission to trauma service, alternative pain management modalities, for example, hip block, multidisciplinary care rounds with integration of the Biopsychosocial Model, evaluation of care approach through research and data analysis, achievement of optimal level of functioning and independence upon discharge.

Electronic Caregiver logo. (PRNewsfoto/Electronic Caregiver)

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via Electronic Caregiver Enters Clinical Trials With G60Trauma.org | Markets Insider

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[ARTICLE] Real-time and wearable functional electrical stimulation system for volitional hand motor function control using the electromyography bridge method. – Full Text

Abstract

Voluntary participation of hemiplegic patients is crucial for functional electrical stimulation therapy. A wearable functional electrical stimulation system has been proposed for real-time volitional hand motor function control using the electromyography bridge method. Through a series of novel design concepts, including the integration of a detecting circuit and an analog-to-digital converter, a miniaturized functional electrical stimulation circuit technique, a low-power super-regeneration chip for wireless receiving, and two wearable armbands, a prototype system has been established with reduced size, power, and overall cost. Based on wrist joint torque reproduction and classification experiments performed on six healthy subjects, the optimized surface electromyography thresholds and trained logistic regression classifier parameters were statistically chosen to establish wrist and hand motion control with high accuracy. Test results showed that wrist flexion/extension, hand grasp, and finger extension could be reproduced with high accuracy and low latency. This system can build a bridge of information transmission between healthy limbs and paralyzed limbs, effectively improve voluntary participation of hemiplegic patients, and elevate efficiency of rehabilitation training.

Introduction

Functional electrical stimulation (FES) has been introduced as a neurorehabilitation method to artificially activate sensory and motor systems following central nervous system disease or injury, such as spinal cord injury and stroke (Popović, 2014; Shen et al., 2016; Wade and Gorgey, 2016). The first noninvasive FES system was used for foot drop correction of hemiplegic patients by Liberson et al. (1961). Many novel FES systems have been designed as surface or implantable stimulation systems for controlling arms and hands (Saxena et al., 1995; Ijzerman et al., 1996; Kilgore et al., 1997; Knutson et al., 2012; Hara et al., 2013).

The NESS Handmaster (Ijzerman et al., 1996) and the FES system (Nathan, 1989) belong to the push-button controlled FES method (or switch-based FES). Both of these methods use on/off stimulation with pre-programmed sequences to help spinal cord injury patients recover hand grasp movements and other daily functions. Electromyography (EMG) has been used for on/off control in EMG-triggered FES (Cauraugh et al., 2005) or proportional EMG-controlled FES (Saxena et al., 1995; Thorsen et al., 2001; Hara et al., 2013) and capitalizes on the principle of intension-driven motion. Therapeutic effects were reduced by approximately half if FES was applied without voluntary recipient involvement (Barsi et al., 2008). Preliminary results (McGie et al., 2015) suggest that motor-evoked potential of brain computer interface-controlled FES (Pfurtscheller et al., 2003) and EMG-controlled FES can elicit greater neuroplastic changes than conventional therapy. However, EMG-controlled FES requires some residual movement of the affected arm or hand, so it is not applicable with severely disabled stroke survivors. Contralaterally controlled FES is a promising therapy designed to improve recovery of paretic limbs after stroke. Two case series pilot studies (Knutson et al., 2009, 2014) and an early-phase randomized controlled trial (Knutson et al., 2012) verified the efficiency of contralaterally controlled FES. However, it is important for the success of FES therapy to include the contralateral limb in volitional control of electrically induced contraction in the affected limb.

Based on the success of volitional control of FES, our group previously designed an FES system for restoring motor function in post-stroke hemiplegic patients (Huang et al., 2014). In that system, a frequency-modulation stimulation algorithm based on surface EMG (sEMG) and the support vector machine model were used. However, sEMG thresholds need to be carefully chosen and force reproduction performance has not yet been established. The system is also too difficult to wear and remove.

The specific objectives of this paper were: (1) to use statistical experiments and analyses to optimize the primary parameter “sEMG thresholds” of the frequency-modulation stimulation generation algorithm formerly proposed by our group and to verify the force reproduction performance; (2) to develop a low-complexity algorithm based on logistic regression for hand movement classification achieved by these sEMG thresholds; (3) to develop a wireless and wearable FES system using the EMG-bridge method for real-time volitional hand motor function control, and to assess the feasibility of this system in real-time control of four hand movements. This novel system is a wearable EMG-bridge system that is distributed via a contralateral sEMG-controlled FES system providing more convenience to use at home. The size, power, and overall cost have been significantly reduced compared with the previous prototype (Huang et al., 2014).

Continue —> Real-time and wearable functional electrical stimulation system for volitional hand motor function control using the electromyography bridge method Wang Hp, Bi Zy, Zhou Y, Zhou Yx, Wang Zg, Lv Xy – Neural Regen Res

Figure 3: Prototype wearable EMG-bridge system.(A) The prototype wearable EMG-bridge system. (B) The radio frequency receiver board. (C) The self-designed integrated super-regenerative receiver chip in 0.35-mm complementary metal oxide semiconductor. (1) Transmitting wearable band; (2) receiving wearable band; (3) on-off keying circuit (radio frequency transmitter); (4) super-regenerative receiver circuit (radio frequency receiver); (5) interface between sEMG electrodes and sEMG signal detecting circuit; (6) interface between functional electrical stimulation circuit and gelled stimulation electrodes; (7) surface Ag/AgCl electrocardiogram electrodes for sEMG signal acquisitions; (8) gelled stimulation electrodes (4 × 4 cm<sup>2</sup>). sEMG: Surface electromyography.

Figure 3: Prototype wearable EMG-bridge system. (A) The prototype wearable EMG-bridge system. (B) The radio frequency receiver board. (C) The self-designed integrated super-regenerative receiver chip in 0.35-mm complementary metal oxide semiconductor. (1) Transmitting wearable band; (2) receiving wearable band; (3) on-off keying circuit (radio frequency transmitter); (4) super-regenerative receiver circuit (radio frequency receiver); (5) interface between sEMG electrodes and sEMG signal detecting circuit; (6) interface between functional electrical stimulation circuit and gelled stimulation electrodes; (7) surface Ag/AgCl electrocardiogram electrodes for sEMG signal acquisitions; (8) gelled stimulation electrodes (4 × 4 cm2). sEMG: Surface electromyography.

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[ARTICLE] Real-time inverse kinematics for the upper limb: a model-based algorithm using segment orientations – Full Text

Abstract

Background

Model based analysis of human upper limb movements has key importance in understanding the motor control processes of our nervous system. Various simulation software packages have been developed over the years to perform model based analysis. These packages provide computationally intensive—and therefore off-line—solutions to calculate the anatomical joint angles from motion captured raw measurement data (also referred as inverse kinematics). In addition, recent developments in inertial motion sensing technology show that it may replace large, immobile and expensive optical systems with small, mobile and cheaper solutions in cases when a laboratory-free measurement setup is needed. The objective of the presented work is to extend the workflow of measurement and analysis of human arm movements with an algorithm that allows accurate and real-time estimation of anatomical joint angles for a widely used OpenSim upper limb kinematic model when inertial sensors are used for movement recording.

Methods

The internal structure of the selected upper limb model is analyzed and used as the underlying platform for the development of the proposed algorithm. Based on this structure, a prototype marker set is constructed that facilitates the reconstruction of model-based joint angles using orientation data directly available from inertial measurement systems. The mathematical formulation of the reconstruction algorithm is presented along with the validation of the algorithm on various platforms, including embedded environments.

Results

Execution performance tables of the proposed algorithm show significant improvement on all tested platforms. Compared to OpenSim’s Inverse Kinematics tool 50–15,000x speedup is achieved while maintaining numerical accuracy.

Conclusions

The proposed algorithm is capable of real-time reconstruction of standardized anatomical joint angles even in embedded environments, establishing a new way for complex applications to take advantage of accurate and fast model-based inverse kinematics calculations.

Continue —> Real-time inverse kinematics for the upper limb: a model-based algorithm using segment orientations | BioMedical Engineering OnLine | Full Text

Fig. 1 Representations of the used upper limb model with reference poses and markers. a Screenshot taken from OpenSim while displaying the used full arm model. The reference configuration is shown as a shaded overlay on an actual example configuration determined by the joint angle vector [θelvθelv = 0∘0∘, θsh_elvθsh_elv = 63∘63∘, θsh_rotθsh_rot = 15∘15∘, θel_flexθel_flex = 95∘95∘, θpro_supθpro_sup = −60∘−60∘, θdev_cθdev_c = 0∘0∘, θflex_cθflex_c = 20∘20∘]. b Representation of the model’s exported structure in MATLAB producing the same actual configuration as in sub-figure (a) using the developed forward kinematics function (functionally equivalent to OpenSim’s version). c Locations of prototype markers that are solely used to the reconstruction of model-defined anatomical joint angles with the proposed algorithm. d Locations of virtual markers that are used for the algorithm validation process by serving as inputs to OpenSim’s inverse kinematics tool directly

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