Posts Tagged Sensor-based

[Abstract + References] Mobile, Exercise-agnostic, Sensor-based Serious Games for Physical Rehabilitation at Home


Serious games can improve the physical rehabilitation of patients with different conditions. By monitoring exercises and offering feedback, serious games promote the correct execution of exercises outside the clinic. Nevertheless, existing serious games are limited to specific exercises, which reduces their practical impact. This paper describes the design of three exercise-agnostic games, that can be used for a multitude of rehabilitation scenarios. The developed games are displayed on a smartphone and are controlled by a wearable device, containing inertial and electromyography sensors. Results from a preliminary evaluation with 10 users are discussed, together with plans for future work.


  1. Steven Dow, Blair MacIntyre, Jaemin Lee, Christopher Oezbek, Jay David Bolter, and Maribeth Gandy. 2005. Wizard of Oz Support Throughout an Iterative Design Process. IEEE Pervasive Computing 4, 4 (Oct. 2005), 18–26. Google ScholarDigital Library
  2. Brook Galna, Dan Jackson, Guy Schofield, Roisin McNaney, Mary Webster, Gillian Barry, Dadirayi Mhiripiri, Madeline Balaam, Patrick Olivier, and Lynn Rochester. 2014. Retraining function in people with Parkinson’s disease using the Microsoft kinect: game design and pilot testing. Journal of NeuroEngineering and Rehabilitation 11, 1 (14 Apr 2014), 60.Google ScholarCross Ref
  3. S.J. Ge_en. 2003. Rehabilitation principles for treating chronic musculoskeletal injuries. Med J Aust 178, 5 (2003), 238–242.Google ScholarCross Ref
  4. Maureen Kerwin, Francisco Nunes, and Paula Alexandra Silva. 2012. Dance! Don’t Fall – preventing falls and promoting exercise at home. Studies in health technology and informatics 177 (2012), 254259. Scholar
  5. K. Laver, S. George, J. Ratcli_e, S. Quinn, C. Whitehead, O. Davies, and M. Crotty. 2011. Use of an interactive video gaming program compared with conventional physiotherapy for hospitalised older adults: a feasibility trial. Disability and Rehabilitation 34, 21 (2011), 1802–1808.Google ScholarCross Ref
  6. Gwyn N. Lewis, Claire Woods, Juliet A. Rosie, and Kathryn M. Mcpherson. 2011. Virtual reality games for rehabilitation of people with stroke: perspectives from the users. Disability and Rehabilitation: Assistive Technology 6, 5 (2011), 453–463.Google ScholarCross Ref
  7. Simon McCallum. 2012. Gami_cation and serious games for personalized health. Stud Health Technol Inform 177 (2012), 85–96.Google Scholar
  8. Brian A. Primack, Mary V. Carroll, Megan McNamara, Mary Lou Klem, Brandy King, Michael Rich, Chun W. Chan, and Smita Nayak. 2012. Role of Video Games in Improving Health-Related Outcomes: A Systematic Review. American Journal of Preventive Medicine 42, 6 (2012), 630–638.Google ScholarCross Ref
  9. A. Santos, V. Guimares, N. Matos, J. Cevada, C. Ferreira, and I. Sousa. 2015. Multi-sensor exercise-based interactive games for fall prevention and rehabilitation. In 9th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth). 65–71. Google ScholarDigital Library
  10. Devinder Kaur Ajit Singh, Nor Azlin Mohd Nordin, Noor Azah Abd Aziz, Beng Kooi Lim, and Li Ching Soh. 2013. E_ects of substituting a portion of standard physiotherapy time with virtual reality games among community-dwelling stroke survivors. BMC Neurology 13, 1 (13 Dec 2013), 199.Google Scholar
  11. Jan David Smeddinck, Marc Herrlich, and Rainer Malaka. 2015. Exergames for Physiotherapy and Rehabilitation: A Medium-term Situated Study of Motivational Aspects and Impact on Functional Reach. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI ’15). ACM, New York, NY, USA, 4143–4146. Google ScholarDigital Library
  12. Gabriele Spina, Guannan Huang, Anouk Vaes, Martijn Spruit, and Oliver Amft. 2013. COPDTrainer: A Smartphone-based Motion Rehabilitation Training System with Real-time Acoustic Feedback. In Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp ’13). ACM, New York, NY, USA, 597–606. Google ScholarDigital Library

via Mobile, Exercise-agnostic, Sensor-based Serious Games for Physical Rehabilitation at Home | Proceedings of the Twelfth International Conference on Tangible, Embedded, and Embodied Interaction

, , , ,

Leave a comment

[REVIEW] On the assessment of coordination between upper extremities: towards a common language between rehabilitation engineers, clinicians and neuroscientists – Full Text


Well-developed coordination of the upper extremities is critical for function in everyday life. Interlimb coordination is an intuitive, yet subjective concept that refers to spatio-temporal relationships between kinematic, kinetic and physiological variables of two or more limbs executing a motor task with a common goal. While both the clinical and neuroscience communities agree on the relevance of assessing and quantifying interlimb coordination, rehabilitation engineers struggle to translate the knowledge and needs of clinicians and neuroscientists into technological devices for the impaired. The use of ambiguous definitions in the scientific literature, and lack of common agreement on what should be measured, present large barriers to advancements in this area. Here, we present the different definitions and approaches to assess and quantify interlimb coordination in the clinic, in motor control studies, and by state-of-the-art robotic devices. We then propose a taxonomy of interlimb activities and give recommendations for future neuroscience-based robotic- and sensor-based assessments of upper limb function that are applicable to the everyday clinical practice. We believe this is the first step towards our long-term goal of unifying different fields and help the generation of more consistent and effective tools for neurorehabilitation.


This work was developed as part of the project “State of the Art Robot-Supported assessments (STARS)” in the frame of the COST Action TD1006 “European Network on Robotics for NeuroRehabilitation” [1]. The goal of STARS is to give neurorehabilitation clinical practitioners and scientists recommendations for the development, implementation, and administration of different indices of robotic assessments, grounded on scientific evidence.

Well-coordinated movements are a characteristic feature of well-developed motor behavior. From neuroscientists to clinicians, quantifying coordination of an individual is of critical importance. Not only does this help in understanding the neurophysiological components of movement (neuroscience field), but it can also help us identify and assess underlying neurological problems of a patient with movement disorders, and guide therapeutic interventions (clinical field).

The term ‘coordination’ is so strongly ingrained in our common language that we do not typically stop to think about the key underlying features that characterize good and bad coordination–even though we can all distinguish the well-coordinated movements of a trained dancer from those of a novice. What exactly is meant by coordination? And how should it be measured? Addressing these questions is particularly difficult when considering such an abstract concept, which encompasses many different aspects that are not straightforward to define formally.

Indeed, coordinated movements are multidimensional and require the organization of multiple subsystems, e.g., eye-hand coordination [2], intersegmental coordination [3], intralimb coordination [4], interlimb coordination [5]. Given the multiple connotations and associations to the word coordination, in this paper, we attempt to summarize how coordination between upper extremities-a form of interlimb coordination-is interpreted and measured by clinicians, neuroscientists and rehabilitation engineers.

As the reader will see in the following pages, the descriptors of interlimb coordination and how it is assessed vary considerably from field to field, and even within a field. This lack of a common language and standard terminology is a huge barrier to relate the observations from different fields, hindering the understanding and discussion needed to move forward. Further, such definitions are critical for engineers working in translational neurorehabilitation, who harness knowledge from basic and clinical neuroscience to produce technological tools (e.g., robotic devices, instrumented tools) to aid clinicians in their everyday practice. The lack of a common understanding has fostered the use of dozens of ad-hoc algorithms and assessment tools (see section 3), most of which have had limited transfer to everyday clinical applications.

Our long-term goal is to standardize the administration of robotic-and sensor-based assessments of sensory-motor function. Towards this end, we present a summary of different ways in which interlimb coordination has been studied and quantified. We start by presenting a general overview of why the study of coordination between upper limbs is relevant for clinicians and behavioral neuroscientists. We then present a summary of how interlimb coordination is typically assessed in clinical environments and during related motor control experiments. This is followed by a proposal of categorization of interlimb tasks and different outcome measures that are applicable to each task. We believe that the growing scientific community in translational neurorehabilitation research would benefit from this condensed review. …

Continue —> On the assessment of coordination between upper extremities: towards a common language between rehabilitation engineers, clinicians and neuroscientists | Journal of NeuroEngineering and Rehabilitation | Full Text

, , , , , , , ,

Leave a comment

%d bloggers like this: