A telerehabilitation system based on wireless motion capture sensors

The constant growth of the elderly population in the world creates new challenges and opportunities in health care systems. New technological solutions have to be found in order to meet the needs and demands of our aging society. The welfare and quality of life of the elderly population must be a priority. Continuous physical activity will play an important role, due to the increase of the retirement age. However, physiotherapy can be expensive, even when the desire movements are autonomous and simple, also requires people to move to rehabilitation centres. Within this context, this paper describes the development and preliminary tests of a wireless sensor network, based on wearable inertial and magnetic sensors, applied to the capture of human motion. This will enable a personalized home-based rehabilitation system for the elderly or people in remote physical locations.Project “AAL4ALL”, co-financed by the European Community Fund FEDER through COMPETE – Programa Operacional Factores de Competitividade (POFC).FCT – Foundation for Science and Technology – Lisbon, Portugal, through project PEst-C/CTM/LA0025/2013

Is It Worth It?: A Review of the Current Diagnostic Tools and Understandings of Athletes Suffering with CTE

Increased media coverage on the potential risk of full-contact sports related concussions leading to Chronic Traumatic Encephalopathy (CTE) has led to a proactive response within the scientific community. Scientists are in search of a way to diagnose and prevent the spread of this neurodegenerative disease. In the past, diagnosis of CTE was primarily confirmed by an extensive autopsy of the brain with a considerable amount of tau protein being a strong indicator of CTE. Finding new biomarker imaging tools to assess an individual\u27s brain prior to death is greatly needed. In the past few years, newly developed tau protein probes have been made available for imaging use in the area of neurodegenerative diseases. In comparison to prior pathological histology findings present in CTE diseased brains, the biomarker probes were found to illuminate tau aggregates in the brain with similar correlating patterns to prior autopsy reports. Biomarkers are available for use in both imaging and blood serum/ cerebrospinal fluid assays for identifying tau protein. Further research in these areas is warranted as the definition and clinical symptoms of CTE become clearer

Music-based biofeedback to reduce tibial shock in over-ground running : a proof-of-concept study

Methods to reduce impact in distance runners have been proposed based on real-time auditory feedback of tibial acceleration. These methods were developed using treadmill running. In this study, we extend these methods to a more natural environment with a proof-of-concept. We selected ten runners with high tibial shock. They used a music-based biofeedback system with headphones in a running session on an athletic track. The feedback consisted of music superimposed with noise coupled to tibial shock. The music was automatically synchronized to the running cadence. The level of noise could be reduced by reducing the momentary level of tibial shock, thereby providing a more pleasant listening experience. The running speed was controlled between the condition without biofeedback and the condition of biofeedback. The results show that tibial shock decreased by 27% or 2.96 g without guided instructions on gait modification in the biofeedback condition. The reduction in tibial shock did not result in a clear increase in the running cadence. The results indicate that a wearable biofeedback system aids in shock reduction during over-ground running. This paves the way to evaluate and retrain runners in over-ground running programs that target running with less impact through instantaneous auditory feedback on tibial shock

EEG Cortical Neuroimaging during Human Full-Body Movement.

Studying how the human brain functions during full-body movement can increase our understanding of how to diagnose and treat neurological disorders. High-density electroencephalography (EEG) can record brain activity during body movement due to its portability and excellent time resolution. However, EEG is prone to movement artifact, and traditional EEG methods have poor spatial resolution. Combining EEG with independent component analysis (ICA) and inverse source modeling can improve spatial resolution. In my first study, I used EEG and ICA to investigate the biomechanical and neural interplay of performing a complicated cognitive task at different walking speeds. Young, healthy subjects stepped significantly wider when walking with the cognitive task compared to walking alone, but walking speed did not affect cognitive performance (i.e. reaction time and correct responses). EEG results mirrored cognitive performance, in that there were similar event-related desynchronizations in the somatosensory association cortex around encoding at all speeds. For my second study, I addressed the problem of movement artifact in EEG. I created an interface that blocked true electrocortical signals while recording only movement artifact. I quantified the spectral changes in the movement artifact EEG, tested various methods of removing the artifact, and compared their efficacies. Artifact spectral power varied across individuals, electrode locations, and walking speed. None of the cleaning methods removed all artifact. For my third study, I examined cortical spectral power fluctuations and effective connectivity during active and viewed full-body exercise with different combinations of arm and leg effort. Larger spectral fluctuations occurred in the cortex during rhythmic arm exercise compared to rhythmic leg exercise, which suggests that rhythmic arm movement is more cortically driven. The strength and direction of information flow was very similar between the active and viewed exercise conditions, with the right motor cortex being the hub of information flow. These studies provide insight into how the human brain functions during full-body movement and may have applications for rehabilitation after a brain injury or in brain monitoring for improving cognitive performance.PhDBiomedical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116622/1/jekline_1.pd

Application of data fusion techniques and technologies for wearable health monitoring

Technological advances in sensors and communications have enabled discrete integration into everyday objects, both in the home and about the person. Information gathered by monitoring physiological, behavioural, and social aspects of our lives, can be used to achieve a positive impact on quality of life, health, and well-being. Wearable sensors are at the cusp of becoming truly pervasive, and could be woven into the clothes and accessories that we wear such that they become ubiquitous and transparent. To interpret the complex multidimensional information provided by these sensors, data fusion techniques are employed to provide a meaningful representation of the sensor outputs. This paper is intended to provide a short overview of data fusion techniques and algorithms that can be used to interpret wearable sensor data in the context of health monitoring applications. The application of these techniques are then described in the context of healthcare including activity and ambulatory monitoring, gait analysis, fall detection, and biometric monitoring. A snap-shot of current commercially available sensors is also provided, focusing on their sensing capability, and a commentary on the gaps that need to be bridged to bring research to market

Identifying Gait Deficits in Stroke Patients Using Inertial Sensors

Falls remain a significant problem for stroke patients. Tripping, the main cause of falls, occurs when there is insufficient clearance between the foot and ground. Based on an individual’s gait deficits, different joint angles and coordination patterns are necessary to achieve adequate foot clearance during walking. However, gait deficits are typically only quantified in a research or clinical setting, and it would be helpful to use wearable devices – such as accelerometers – to quantify gait disorders in real-world situations. Therefore, the objective of this project was to understand gait characteristics that influence the risk of tripping, and to detect these characteristics using accelerometers. Thirty-five participants with a range of walking abilities performed normal walking and attempted to avoid tripping on an unexpected object while gait characteristics were quantified using motion capture techniques and accelerometers. Multiple regression was used to identify the relationship between joint coordination and foot clearance, and multiple analysis of variance was used to determine characteristics of gait that differ between demographic groups, as well as those that enable obstacle avoidance. Machine learning techniques were employed to detect joint angles and the risk of tripping from patterns in accelerometer signals. Measures of foot clearance that represent toe height throughout swing instead of at a single time point are more sensitive to changes in joint coordination, with hip-knee coordination during midswing having the greatest effect. Participants with a history of falls or stroke perform worse than older non-fallers and young adults on many factors related to falls risk, however, there are no differences in the ability to avoid an unexpected obstacle between these groups. Individuals with an inability to avoid an obstacle have lower scores on functional evaluations, exhibit limited sagittal plane joint range of motion during swing, and adopt a conservative walking strategy. Machine learning processes can be used to predict knee range of motion and classify individuals at risk for tripping based on an ankle-worn accelerometer. This work is significant because a portable device that detects gait characteristics relevant to the risk of tripping without expensive motion capture technology may reduce the risk of falls for stroke patients

A review of activity trackers for senior citizens: research perspectives, commercial landscape and the role of the insurance industry

The objective assessment of physical activity levels through wearable inertial-based motion detectors for the automatic, continuous and long-term monitoring of people in free-living environments is a well-known research area in the literature. However, their application to older adults can present particular constraints. This paper reviews the adoption of wearable devices in senior citizens by describing various researches for monitoring physical activity indicators, such as energy expenditure, posture transitions, activity classification, fall detection and prediction, gait and balance analysis, also by adopting consumer-grade fitness trackers with the associated limitations regarding acceptability. This review also describes and compares existing commercial products encompassing activity trackers tailored for older adults, thus providing a comprehensive outlook of the status of commercially available motion tracking systems. Finally, the impact of wearable devices on life and health insurance companies, with a description of the potential benefits for the industry and the wearables market, was analyzed as an example of the potential emerging market drivers for such technology in the future