Now showing 1 - 8 of 8
  • Publication
    Biofeedback in Breast Cancer Rehabilitation: Applying the WHO ICF Core Set to Identify Opportunities and Recommendations
    Digital biofeedback technologies are used in physical rehabilitation to improve motor learning and enhance engagement with therapies, but they are unfrequently used in breast cancer rehabilitation. Digital biofeedback interventions should be custom-made for the specific breast cancer context. The WHO ICF Core Set for Breast Cancer describes this context by itemising the biopsychosocial and environmental factors associated with breast cancer. We analysed this Core Set to identify opportunities for biofeedback intervention, and to make recommendations for successful, inclusive design of digital biofeedback interventions in breast cancer rehabilitation. Impairments of strength, joint movement and upper limb function present opportunities for the development of digital biofeedback interventions. Factors related to sensory loss, lymphoedema, chemotherapy-related cognitive dysfunction and fatigue should be considered when designing and evaluating biofeedback systems.
      513Scopus© Citations 1
  • Publication
    A Research Roadmap: Connected Health as an Enabler of Cancer Patient Support
    The evidence that quality of life is a positive variable for the survival of cancer patients has prompted the interest of the health and pharmaceutical industry in considering that variable as a final clinical outcome. Sustained improvements in cancer care in recent years have resulted in increased numbers of people living with and beyond cancer, with increased attention being placed on improving quality of life for those individuals. Connected Health provides the foundations for the transformation of cancer care into a patient-centric model, focused on providing fully connected, personalized support and therapy for the unique needs of each patient. Connected Health creates an opportunity to overcome barriers to health care support among patients diagnosed with chronic conditions. This paper provides an overview of important areas for the foundations of the creation of a new Connected Health paradigm in cancer care. Here we discuss the capabilities of mobile and wearable technologies; we also discuss pervasive and persuasive strategies and device systems to provide multidisciplinary and inclusive approaches for cancer patients for mental well-being, physical activity promotion, and rehabilitation. Several examples already show that there is enthusiasm in strengthening the possibilities offered by Connected Health in persuasive and pervasive technology in cancer care. Developments harnessing the Internet of Things, personalization, patient-centered design, and artificial intelligence help to monitor and assess the health status of cancer patients. Furthermore, this paper analyses the data infrastructure ecosystem for Connected Health and its semantic interoperability with the Connected Health economy ecosystem and its associated barriers. Interoperability is essential when developing Connected Health solutions that integrate with health systems and electronic health records. Given the exponential business growth of the Connected Health economy, there is an urgent need to develop mHealth (mobile health) exponentially, making it both an attractive and challenging market. In conclusion, there is a need for user-centered and multidisciplinary standards of practice to the design, development, evaluation, and implementation of Connected Health interventions in cancer care to ensure their acceptability, practicality, feasibility, effectiveness, affordability, safety, and equity.
      520Scopus© Citations 14
  • Publication
    Rehabilitation Exercise Segmentation for Autonomous Biofeedback Systems with ConvFSM
    Segmenting physical movements is a key step for any accelerometry-based autonomous biofeedback system oriented to rehabilitation and physiotherapy activities. Fundamentally, this can be reduced to the detection of recurrent patterns, also called motion primitives, in longer inertial signals. Most of the solutions developed in the literature require extensive domain knowledge, or are incapable of scaling to complex motion patterns and new exercises. In this paper, we explore the capabilities of inertial measurement units for the segmentation of upper limb rehabilitation exercises. To do so, we introduce a novel segmentation technique based on Convolutional Neural Networks and Finite State Machines, called ConvFSM. ConvFSM is able to isolate motion primitives from raw streaming data, using very little domain knowledge. We also investigate different combinations of sensors, in order to identify the most effective and flexible setup that could fit a home-based rehabilitation feedback system. Experimental results are presented, based on a dataset obtained from a combination of common upper limb and lower limb exercises.
      509Scopus© Citations 6
  • Publication
    Segmentation of shoulder rehabilitation exercises for single and multiple inertial sensor systems
    Introduction:Digital home rehabilitation systems require accurate segmentation methods to provide appropriate feedback on repetition counting and exercise technique. Current segmentation methods are not suitable for clinical use; they are not highly accurate or require multiple sensors, which creates usability problems. We propose a model for accurately segmenting inertial measurement unit data for shoulder rehabilitation exercises. This study aims to use inertial measurement unit data to train and test a machine learning segmentation model for single- and multiple-inertial measurement unit systems and to identify the optimal single-sensor location. Methods:A focus group of specialist physiotherapists selected the exercises, which were performed by participants wearing inertial measurement units on the wrist, arm and scapula. We applied a novel machine learning based segmentation technique involving a convolutional classifier and Finite State Machine to the inertial measurement unit data. An accuracy score was calculated for each possible single- or multiple-sensor system. Results:The wrist inertial measurement unit was chosen as the optimal single-sensor location for future system development (mean overall accuracy 0.871). Flexion and abduction based exercises mostly could be segmented with high accuracy, but scapular movement exercises had poor accuracy. Conclusion:A wrist-worn single inertial measurement unit system can accurately segment shoulder exercise repetitions; however, accuracy varies depending on characteristics of the exercise.
      319
  • Publication
    Conceptualising a Targeted Rehabilitation Exercise Biofeedback System for a Cancer Survivorship Population
    Introduction The increased prevalence of cancer survivors requires a focus on developing long-term, cost-effective management strategies to prevent and limit disability and morbidity. Background Cancer survivors with pain, weakness and restricted movement often benefit from targeted exercise programmes provided by a Physiotherapist. Physical, psychological and situational factors can impact on patients abilities to complete these exercises. In recent years, interactive biofeedback exercise systems have been shown to be effective in the rehabilitation of musculoskeletal impairments. Such a system has not yet been developed for cancer survivors. Proposal An targeted rehabilitation exercise biofeedback system for use in cancer survivors is proposed. This system aims to enhance rehabilitation outcomes using biofeedback, gamification and adherence promotion strategies. Conclusion An online targeted-exercise biofeedback system for use in cancer rehabilitation would be an innovative, beneficial development for the growing numbers of individuals surviving cancer.
      290
  • Publication
    The use of neuromuscular electrical stimulation (NMES) for managing the complications of ageing related to reduced exercise participation
    Exercise participation and activity levels are low in many older adults, and when paired with the multi-systemic effects of ageing, such as sarcopenia and decreased cardiovascular function, can result in a loss of functional independence. Voluntary exercise may not always be feasible for these individuals, highlighting a need for alternative therapies. There is a growing body of literature that recognises the positive effects of neuromuscular electrical stimulation (NMES) on muscle strength, muscle mass and cardiorespiratory function in older adults. However, NMES suffers from poor clinical acceptability due to multiple barriers to its use, and poor patient engagement and adherence have been noted. Technology-based supports to exercise, such as biofeedback and ‘gamification’, have been effectively paired with a variety of rehabilitation interventions. This suggests that these supports could be promising additions to an NMES exercise system to reduce barriers to its use and maximise clinical outcomes.
      840Scopus© Citations 14
  • Publication
    Feedback Design in Targeted Exercise Digital Biofeedback Systems for Home Rehabilitation: A Scoping Review
    Digital biofeedback systems (DBSs) are used in physical rehabilitation to improve outcomes by engaging and educating patients and have the potential to support patients while doing targeted exercises during home rehabilitation. The components of feedback (mode, content, frequency and timing) can influence motor learning and engagement in various ways. The feedback design used in DBSs for targeted exercise home rehabilitation, as well as the evidence underpinning the feedback and how it is evaluated, is not clearly known. To explore these concepts, we conducted a scoping review where an electronic search of PUBMED, PEDro and ACM digital libraries was conducted from January 2000 to July 2019. The main inclusion criteria included DBSs for targeted exercises, in a home rehabilitation setting, which have been tested on a clinical population. Nineteen papers were reviewed, detailing thirteen different DBSs. Feedback was mainly visual, concurrent and descriptive, frequently providing knowledge of results. Three systems provided clear rationale for the use of feedback. Four studies conducted specific evaluations of the feedback, and seven studies evaluated feedback in a less detailed or indirect manner. Future studies should describe in detail the feedback design in DBSs and consider a robust evaluation of the feedback element of the intervention to determine its efficacy.
      319Scopus© Citations 23
  • Publication
    Patient Experiences of Rehabilitation and the Potential for an mHealth System with Biofeedback After Breast Cancer Surgery: Qualitative Study
    BACKGROUND: Physiotherapy-led home rehabilitation after breast cancer surgery can protect against the development of upper limb dysfunction and other disabling consequences of surgery. A variety of barriers can limit physical rehabilitation outcomes, and patients may benefit from more support during this time. Mobile health (mHealth) systems can assist patients during rehabilitation by providing exercise support, biofeedback, and information. Before designing mHealth systems for a specific population, developers must first engage with users to understand their experiences and needs. OBJECTIVE: The aims of this study were to explore patients' rehabilitation experiences and unmet needs during home rehabilitation after breast cancer surgery and to understand their experiences of mHealth technology and the requirements they desire from an mHealth system. METHODS: This was the first stage of a user-centered design process for an mHealth system. We interviewed 10 breast cancer survivors under the two main topics of "Rehabilitation" and "Technology" and performed a thematic analysis on the interview data. RESULTS: Discussions regarding rehabilitation focused on the acute and long-term consequences of surgery; unmet needs and lack of support; self-driven rehabilitation; and visions for high-quality rehabilitation. Regarding technology, participants reported a lack of mHealth options for this clinical context and using non-cancer-specific applications and wearables. Participants requested an mHealth tool from a reliable source that provides exercise support. CONCLUSIONS: There are unmet needs surrounding access to physiotherapy, information, and support during home rehabilitation after breast cancer surgery that could be addressed with an mHealth system. Breast cancer survivors are open to using an mHealth system and require that it comes from a reliable source and focuses on supporting exercise performance.
      289Scopus© Citations 16