Gearóid ÓLaighin

Direct measurement of human movement by accelerometry

Human movement has been the subject of investigation since the fifth century when early scientists and researchers attempted to model the human musculoskeletal system. The anatomical complexities of the human body have made it a constant source of research to this day with many anatomical, physiological, mechanical, environmental, sociological and psychological studies undertaken to define its key elements. These studies have utilised modern day techniques to assess human movement in many illnesses. One such modern technique has been direct measurement by accelerometry, which was first suggested in the 1970s but has only been refined and perfected during the last 10-15 years. Direct measurement by accelerometry has seen the introduction of the successful implementation of low power, low cost electronic sensors that have been employed in clinical and home environments for the constant monitoring of patients (and their controls). The qualitative and quantitative data provided by these sensors make it possible for engineers, clinicians and physicians to work together to be able to help their patients in overcoming their physical disability. This paper presents the underlying biomechanical elements necessary to understand and study human movement. It also reflects on the sociological elements of human movement and why it is important in patient life and well being. Finally the concept of direct measurement by accelerometry is presented with past studies and modern techniques used for data analysis.

Evaluation of waist-mounted tri-axial accelerometer based fall-detection algorithms during scripted and continuous unscripted activities.

It is estimated that by 2050 more than one in five people will be aged 65 or over. In this age group, falls are one of the most serious life-threatening events that can occur. Their automatic detection would help reduce the time of arrival of medical attention, thus reducing the mortality rate and in turn promoting independent living. This study evaluated a variety of existing and novel fall-detection algorithms for a waist-mounted accelerometer based system. In total, 21 algorithms of varying degrees of complexity were tested against a comprehensive data-set recorded from 10 young healthy volunteers performing 240 falls and 120 activities of daily living (ADL) and 10 elderly healthy volunteers performing 240 scripted ADL and 52.4 waking hours of continuous unscripted normal ADL. Results show that using an algorithm that employs thresholds in velocity, impact and posture (velocity+impact+posture) achieves 100% specificity and sensitivity with a false-positive rate of less than 1 false-positive (0.6 false-positives) per day of waking hours. This algorithm is the most suitable method of fall-detection, when tested using continuous unscripted activities performed by elderly healthy volunteers, which is the target environment for a fall-detection device.

The identification of vertical velocity profiles using an inertial sensor to investigate pre-impact detection of falls

This study investigates distinguishing falls from normal Activities of Daily Living (ADL) by thresholding of the vertical velocity of the trunk. Also presented is the design and evaluation of a wearable inertial sensor, capable of accurately measuring these vertical velocity profiles, thus providing an alternative to optical motion capture systems. Five young healthy subjects performed a number of simulated falls and normal ADL and their trunk vertical velocities were measured by both the optical motion capture system and the inertial sensor. Through vertical velocity thresholding (VVT) of the trunk, obtained from the optical motion capture system, at -1.3 m/s, falls can be distinguished from normal ADL, with 100% accuracy and with an average of 323 ms prior to trunk impact and 140 ms prior to knee impact, in this subject group. The vertical velocity profiles obtained using the inertial sensor, were then compared to those obtained using the optical motion capture system. The signals from the inertial sensor were combined to produce vertical velocity profiles using rotational mathematics and integration. Results show high mean correlation (0.941: Coefficient of Multiple Correlations) and low mean percentage error (6.74%) between the signals generated from the inertial sensor to those from the optical motion capture system. The proposed system enables vertical velocity profiles to be measured from elderly subjects in a home environment where as this has previously been impractical.

Effectiveness of a smartphone application to promote physical activity in primary care: the SMART MOVE randomised controlled trial

BACKGROUND Physical inactivity is a major, potentially modifiable, risk factor for cardiovascular disease, cancer, and other chronic diseases. Effective, simple, and generalisable interventions that will increase physical activity in populations are needed. AIM To evaluate the effectiveness of a smartphone application (app) to increase physical activity in primary care. DESIGN AND SETTING An 8-week, open-label, randomised controlled trial in rural, primary care in the west of Ireland. METHOD Android smartphone users >16 years of age were recruited. All participants were provided with similar physical activity goals and information on the benefits of exercise. The intervention group was provided with a smartphone app and detailed instructions on how to use it to achieve these goals. The primary outcome was change in physical activity, as measured by a daily step count between baseline and follow-up. RESULTS A total of 139 patients were referred by their primary care health professional or self-referred. In total, 37 (27%) were screened out and 12 (9%) declined to participate, leaving 90 (65%) patients who were randomised. Of these, 78 provided baseline data (intervention = 37; control = 41) and 77 provided outcome data (intervention = 37; control = 40). The mean daily step count at baseline for intervention and control groups was 4365 and 5138 steps per day respectively. After adjusting, there was evidence of a significant treatment effect (P = 0.009); the difference in mean improvement in daily step count from week 1 to week 8 inclusive was 1029 (95% confidence interval 214 to 1843) steps per day, favouring the intervention. Improvements in physical activity in the intervention group were sustained until the end of the trial. CONCLUSION A simple smartphone app significantly increased physical activity over 8 weeks in a primary care population.

Activity classification using a single chest mounted tri-axial accelerometer

Accelerometer-based activity monitoring sensors have become the most suitable means for objective assessment of mobility trends within patient study groups. The use of minimal, low power, IC (integrated circuit) components within these sensors enable continuous (long-term) monitoring which provides more accurate mobility trends (over days or weeks), reduced cost, longer battery life, reduced size and weight of sensor. Using scripted activities of daily living (ADL) such as sitting, standing, walking, and numerous postural transitions performed under supervised conditions by young and elderly subjects, the ability to discriminate these ADL were investigated using a single tri-axial accelerometer, mounted on the trunk. Data analysis was performed using Matlab® to determine the accelerations performed during eight different ADL. Transitions and transition types were detected using the scalar (dot) product technique and vertical velocity estimates on a single tri-axial accelerometer was compared to a proven discrete wavelet transform method that incorporated accelerometers and gyroscopes. Activities and postural transitions were accurately detected by this simplified low-power kinematic sensor and activity detection algorithm with a sensitivity and specificity of 86-92% for young healthy subjects in a controlled setting and 83-89% for elderly healthy subjects in a home environment.

Monitoring human health behaviour in one's living environment: A technological review

The electronic monitoring of human health behaviour using computer techniques has been an active research area for the past few decades. A wide array of different approaches have been investigated using various technologies including inertial sensors, Global Positioning System, smart homes, Radio Frequency IDentification and others. It is only in recent years that research has turned towards a sensor fusion approach using several different technologies in single systems or devices. These systems allow for an increased volume of data to be collected and for activity data to be better used as measures of behaviour. This change may be due to decreasing hardware costs, smaller sensors, increased power efficiency or increases in portability. This paper is intended to act as a reference for the design of multi-sensor behaviour monitoring systems. The range of technologies that have been used in isolation for behaviour monitoring both in research and commercial devices are reviewed and discussed. Filtering, range, sensitivity, usability and other considerations of different technologies are discussed. A brief overview of commercially available activity monitors and their technology is also included.

Testing of a long-term fall detection system incorporated into a custom vest for the elderly

A fall detection system and algorithm, incorporated into a custom designed garment has been developed. The developed fall detection system uses a tri-axial accelerometer to detect impacts and monitor posture. This sensor is attached to a custom designed vest, designed to be worn by the elderly person under clothing. The fall detection algorithm was developed and incorporates both impact and posture detection capability. The vest and fall algorithm was tested by two teams of 5 elderly subjects who wore the sensor system in turn for 2 week each and were monitored for 8 hours a day.

The past, present and future in scaffold-based tendon treatments

Tendon injuries represent a significant clinical burden on healthcare systems worldwide. As the human population ages and the life expectancy increases, tendon injuries will become more prevalent, especially among young individuals with long life ahead of them. Advancements in engineering, chemistry and biology have made available an array of three-dimensional scaffold-based intervention strategies, natural or synthetic in origin. Further, functionalisation strategies, based on biophysical, biochemical and biological cues, offer control over cellular functions; localisation and sustained release of therapeutics/biologics; and the ability to positively interact with the host to promote repair and regeneration. Herein, we critically discuss current therapies and emerging technologies that aim to transform tendon treatments in the years to come.

SMART MOVE - a smartphone-based intervention to promote physical activity in primary care: study protocol for a randomized controlled trial

BackgroundSedentary lifestyles are now becoming a major concern for governments of developed and developing countries with physical inactivity related to increased all-cause mortality, lower quality of life, and increased risk of obesity, diabetes, hypertension and many other chronic diseases. The powerful onboard computing capacity of smartphones, along with the unique relationship individuals have with their mobile phones, suggests that mobile devices have the potential to influence behavior. However, no previous trials have been conducted using smartphone technology to promote physical activity. This project has the potential to provide robust evidence in this area of innovation. The aim of this study is to evaluate the effectiveness of a smartphone application as an intervention to promote physical activity in primary care.Methods/designA two-group, parallel randomized controlled trial (RCT) with a main outcome measure of mean difference in daily step count between baseline and follow up over eight weeks. A minimum of 80 active android smartphone users over 16 years of age who are able to undertake moderate physical activity are randomly assigned to the intervention group (n = 40) or to a control group (n = 40) for an eight week period. After randomization, all participants will complete a baseline period of one week during which a baseline mean daily step count will be established. The intervention group will be instructed in the usability features of the smartphone application, will be encouraged to try to achieve 10,000 steps per day as an exercise goal and will be given an exercise promotion leaflet. The control group will be encouraged to try to walk an additional 30 minutes per day along with their normal activity (the equivalent of 10,000 steps) as an exercise goal and will be given an exercise promotion leaflet. The primary outcome is mean difference in daily step count between baseline and follow-up. Secondary outcomes are systolic and diastolic blood pressure, resting heart rate, mental health score using HADS and quality of life score using Euroqol. Randomization and allocation to the intervention and groups will be carried out by an independent researcher, ensuring the allocation sequence is concealed from the study researchers until the interventions are assigned. The primary analysis is based on mean daily step count, comparing the mean difference in daily step count between the baseline and the trial periods in the intervention and control groups at follow up.Trial registrationCurrent Controlled Trials ISRCTN99944116

The relationship between cardiac output and dynamic cerebral autoregulation in humans

Cerebral autoregulation adjusts cerebrovascular resistance in the face of changing perfusion pressures to maintain relatively constant flow. Results from several studies suggest that cardiac output may also play a role. We tested the hypothesis that cerebral blood flow would autoregulate independent of changes in cardiac output. Transient systemic hypotension was induced by thigh-cuff deflation in 19 healthy volunteers (7 women) in both supine and seated positions. Mean arterial pressure (Finapres), cerebral blood flow (transcranial Doppler) in the anterior (ACA) and middle cerebral artery (MCA), beat-by-beat cardiac output (echocardiography), and end-tidal Pco(2) were measured. Autoregulation was assessed using the autoregulatory index (ARI) defined by Tiecks et al. (Tiecks FP, Lam AM, Aaslid R, Newell DW. Stroke 26: 1014-1019, 1995). Cerebral autoregulation was better in the supine position in both the ACA [supine ARI: 5.0 ± 0.21 (mean ± SE), seated ARI: 3.9 ± 0.4, P = 0.01] and MCA (supine ARI: 5.0 ± 0.2, seated ARI: 3.8 ± 0.3, P = 0.004). In contrast, cardiac output responses were not different between positions and did not correlate with cerebral blood flow ARIs. In addition, women had better autoregulation in the ACA (P = 0.046), but not the MCA, despite having the same cardiac output response. These data demonstrate cardiac output does not appear to affect the dynamic cerebral autoregulatory response to sudden hypotension in healthy controls, regardless of posture. These results also highlight the importance of considering sex when studying cerebral autoregulation.