Smart Web-Based Platform to Support Physical Rehabilitation

Human Activity Recognition with an HMM-Based Generative Model

Human activity recognition (HAR) has become an interesting topic in healthcare. This application is important in various domains, such as health monitoring, supporting elders, and disease diagnosis. Considering the increasing improvements in smart devices, large amounts of data are generated in our daily lives. In this work, we propose unsupervised, scaled, Dirichlet-based hidden Markov models to analyze human activities. Our motivation is that human activities have sequential patterns and hidden Markov models (HMMs) are some of the strongest statistical models used for modeling data with continuous flow. In this paper, we assume that emission probabilities in HMM follow a bounded-scaled Dirichlet distribution, which is a proper choice in modeling proportional data. To learn our model, we applied the variational inference approach. We used a publicly available dataset to evaluate the performance of our proposed model.

Technological Developments of Mobility in Smart Cities. An Economic Approach

This article introduces the concern that exists in the wider economic world concerning the developments carried out in Smart Cities. The various studies that have been developed capture the economic approach by focusing on specific economic development theories. This article initially provides a theoretical response to the need for a joint approach to the different economic theories relating to Smart Cities, placing the bases of their development in the circular economy. Subsequently, the paper presents a device-based proposal to validate the sustainability principles indicated in the Smart Economy, focusing exclusively on the areas of health and mobility. As a whole, the work concludes with the need to incorporate sustainability criteria into economic ambition so that technological developments have a place in future Smart Cities.

A survey of research trends in assistive technologies using information modelling techniques

BACKGROUND

Despite the rapid proliferation and emphasis on technology, the use of assistive technology among individuals with varying disabilities and age is different. This situation instigates the need for a systematic review to gain a realistic understanding of prominent issues, research trends and assistive technology applications with minimal bias.

OBJECTIVE

Identification of leading researchers and prominent publications in assistive technologies. Subsequently, semantic relation between qualitative and quantitative research literature on assistive technologies was explored to future research directions.

METHODS

A manual search across reputed research databases was done to find out relevant literature from January 2005 to April 2020. In this paper, latent semantic analysis (LSA) was done to develop an information model for achieving defined objectives.

RESULTS

A corpus of 367 research papers published during 2005-2020 was processed using LSA. Term frequency, inverse document frequency of high loading terms provided five major topic solutions. Marcia Scherer, Rory Cooper and Stefano Federici are most noticed authors in assistive technology research. "Smart Assistive Technologies" and "Wearable Technologies for Rehabilitation" came out as contemporary research trends within assistive technologies.

CONCLUSIONS

The manuscript concludes the fact that assistive technologies for rehabilitation are experiencing a transition from standalone mechanical devices towards smart, wearable and connected devices.Implications for RehabilitationCustomized assistive devices could be programmed for multiple uses.User data privacy and internet dependency of smart assistive technologies must be taken care of while designing smart assistive devices for rehabilitation.Fog devices could eliminate the latency issues associated with cloud-based rehabilitation services.

Rehabilitation Exergames: Use of Motion Sensing and Machine Learning to Quantify Exercise Performance in Healthy Volunteers

Background

Performing physiotherapy exercises in front of a physiotherapist yields qualitative assessment notes and immediate feedback. However, practicing the exercises at home lacks feedback on how well patients are performing the prescribed tasks. The absence of proper feedback might result in patients performing the exercises incorrectly, which could worsen their condition. We present an approach to generate performance scores to enable tracking the progress by both the patient at home and the physiotherapist in the clinic.

Objective

This study aims to propose the use of 2 machine learning algorithms, dynamic time warping (DTW) and hidden Markov model (HMM), to quantitatively assess the patient’s performance with respect to a reference.

Methods

Movement data were recorded using a motion sensor (Kinect V2), capable of detecting 25 joints in the human skeleton model, and were compared with those of a reference. A total of 16 participants were recruited to perform 4 different exercises: shoulder abduction, hip abduction, lunge, and sit-to-stand exercises. Their performance was compared with that of a physiotherapist as a reference.

Results

Both algorithms showed a similar trend in assessing participant performance. However, their sensitivity levels were different. Although DTW was more sensitive to small changes, HMM captured a general view of the performance, being less sensitive to the details.

Conclusions

The chosen algorithms demonstrated their capacity to objectively assess the performance of physical therapy. HMM may be more suitable in the early stages of a physiotherapy program to capture and report general performance, whereas DTW could be used later to focus on the details. The scores enable the patient to monitor their daily performance. They can also be reported back to the physiotherapist to track and assess patient progress, provide feedback, and adjust the exercise program if needed.

Design and Validation of Rule-Based Expert System by Using Kinect V2 for Real-Time Athlete Support

In sports and rehabilitation processes where isotonic movements such as bodybuilding are performed, it is vital for individuals to be able to correct the wrong movements instantly by monitoring the trainings simultaneously, and to be able to train healthily and away from the risks of injury. For this purpose, we designed a new real-time athlete support system using Kinect V2 and Expert System. Lateral raise (LR) and dumbbell shoulder press (DSP) movements were selected as examples to be modeled in the system. Kinect V2 was used to obtain angle and distance changes in the shoulder, elbow, wrist, hip, knee, and ankle during movements in these movement models designed. For the rule base of Expert System developed according to these models, a 28-state rule table was designed, and 12 main rules were determined that could be used for both actions. In the sample trainings, it was observed that the decisions made by the system had 89% accuracy in DSP training and 82% accuracy in LR training. In addition, the developed system has been tested by 10 participants (25.8 ± 5.47 years; 74.69 ± 14.81 kg; 173.5 ± 9.52 cm) in DSP and LR training for four weeks. At the end of this period and according to the results of paired t-test analysis (p < 0.05) starting from the first week, it was observed that the participants trained more accurately and that they enhanced their motions by 58.08 ± 11.32% in LR training and 54.84 ± 12.72% in DSP training.

Changes in Discharge to Rehabilitation: Potential Unintended Consequences of Medicare Total Hip Arthroplasty/Total Knee Arthroplasty Bundled Payments, Should They Be Implemented on a Nationwide Scale?

BACKGROUND

As a part of the 2010 Affordable Care Act, Medicare was committed to changing 50% of its reimbursement to alternative payment models by 2018. One strategy included introduction of "bundled payments" or a fixed price for an episode of care. Early studies of the first operative bundles for elective total hip and knee arthroplasty (THA/TKA) suggest changes in discharge to rehabilitation. It remains unclear the extent to which such changes affect patient well-being. In order to address these concerns, the objective of this study is to estimate projected changes in discharge to various type of rehabilitation, 90-day outcomes, extent of therapy received, and patient health-related quality-of-life before and after introduction of bundled payments should they be implemented on a nationwide scale.

METHODS

A nationwide policy simulation was conducted using decision-tree methodology in order to estimate changes in overt and patient-centered outcomes. Model parameters were informed by published research on bundled payment effects and anticipated outcomes of patients discharged to various types of rehabilitation.

RESULTS

Following bundled payment introduction, discharge to inpatient rehabilitation facilities decreased by 16.9 percentage-points (95% confidence interval [CI] 16.5-17.3) among primary TKA patients (THA 16.8 percentage-points), a relative decline from baseline of 58.9%. Skilled nursing facility use fell by 24.0 percentage-points (95% CI 23.6-24.4). It was accompanied by a 36.7 percentage-point (95% CI 36.3-37.2) increase in home health agency use. Although simulation models predicted minimal changes in overt outcome measures such as unplanned readmission (TKA +0.8 percentage-points), changes in discharge disposition were accompanied by significant increases in the need for further assistive care (TKA +8.0 percentage-points) and decreases in patients' functional recovery and extent of therapy received. They collectively accounted for a 30% reduction in recovered motor gains.

CONCLUSION

The results demonstrate substantial changes in discharge to rehabilitation with accompanying declines in average functional outcomes, extent of therapy received, and health-related quality-of-life. Such findings challenge notions of reduced cost at no harm previously attributed to the bundled payment program and lend credence to concerns about reductions in access to facility-based rehabilitation.

An Agile Approach to Improve the Usability of a Physical Telerehabilitation Platform

The goal of a telerehabilitation platform is to safely and securely facilitate the rehabilitation of patients through the use of telecommunication technologies complemented with the use of biomedical smart sensors. The purpose of this study was to perform a usability evaluation of a telerehabilitation platform. To improve the level of usability, the researchers developed and proposed an iterative process. The platform uses a digital representation of the patient which duplicates the therapeutic exercise being executed by the patient; this is detected by a Kinect camera and sensors in real time. This study used inspection methods to perform a usability evaluation of an exploratory prototype of a telerehabilitation platform. In addition, a cognitive workload assessment was performed to complement the usability evaluation. Users were involved through all the stages of the iterative refinement process. Usability issues were progressively reduced from the first iteration to the fourth iteration according to improvements which were developed and applied by the experts. Usability issues originally cataloged as catastrophic were reduced to zero, major usability problems were reduced to ten (2.75%) and minor usability problems were decreased to 141 (38.74%). This study also intends to serve as a guide to improve the usability of e-Health systems in alignment with the software development cycle.

Implementation and Assessment of an Intelligent Motor Tele-Rehabilitation Platform

Over the past few years, software applications for medical assistance, including tele-rehabilitation, have known an increasing presence in the health arena. Despite the several therapeutic and economic advantages of this new paradigm, it is important to follow certain guidelines, in order to build a safe, useful, scalable, and ergonomic tool. This work proposes to address all these points, through the case study of a physical tele-rehabilitation platform for patients after hip replacement surgery. The scalability and versatility of the system is handled by the implementation of a modular architecture. The safeness and effectiveness of the tool is ensured by an artificial intelligence module that assesses the quality of the movements performed by the user. The usability of the application is evaluated by a cognitive walkthrough method. Results show that the system (i) is able to properly assess the correctness of the human’s motion through two possible methods (Dynamic Time Warping and Hidden Markov Model), and (ii) provides a good user experience. The discussion addresses (i) the advantages and disadvantages of the main approaches for a gesture recognition of therapeutic movements, and (ii) critical aspects to provide the patient with the best usability of a tele-rehabilitation platform.