Changes in the electrical properties of the electrode-skin-underlying tissue composite during a week-long programme of neuromuscular electrical stimulation.

Particular neuromuscular electrical stimulation (NMES) applications require the use of the same electrodes over a long duration (>1 day) without having access to them. Under such circumstance the quality of the electrode-skin contact cannot be assessed. We used the NMES signal itself to assess the quality of the electrode-skin contact and the electrical properties of the underlying tissues over a week. A 14% decrease in the skin's stratum corneum resistance (from 20 to 17 kΩ) and a 15% decrease in the resistance of the electrodes and underlying tissues (from 550 to 460 Ω) were observed in the 14 healthy subjects investigated. A follow-on investigation of the effect of exercise-induced sweating on the electrical properties of the electrode-skin-underlying tissue composite during NMES indicated a correlation between the decrease in the resistance values observed over the course of the week and the accumulation of sweat at the electrode-skin interface. The value of the capacitance representing the dielectric properties of the skin's stratum corneum increased after exercise-induced sweating but did not change significantly over the course of the week. We conclude that valuable information about the electrode-skin-underlying tissue composite can be gathered using the NMES signal itself, and suggest that this is a practical, safe and relatively simple method for monitoring these electrical properties during long-term stimulation.

The effect of temperature elevation on cryopreserved mesenchymal stem cells.

BACKGROUND: Cryopreservation is of particular importance in stem cell research and regenerative medicine as it permits long term stabilisation of biological cells. Cells retain their regenerative capacity after years of storage at cryogenic temperatures. However, elevation of temperature may occur due to variety of reasons, for example in the event of equipment malfunction or during delays in transportation. To date, a limited amount research has been carried out to examine the effects of temperature elevation on stem cell survival during cryopreservation. METHODS: Mesenchymal Stem Cells (MSCs) obtained from 8-12 week Sprague Dawley male rats were cryopreserved according to the standard procedures. Under experimental conditions, cryopreserved specimens were exposed to elevated temperatures ranging from -20 C to 37 C and cellular membrane integrity assessed via trypan-blue exclusion at various time points. RESULTS: An approximating model of multiple regression was fitted to the experimental data and optimisation of model parameters was carried out. This model provides an approximation of cell viability in response to elevated temperature conditions. DISCUSSION: The results demonstrate that elevation of temperature has a dramatic effect, even over short periods of time, on the viability of cryopreserved specimens. The model presented here could be used to predict the damage suffered by a specimen due to exposure to elevated temperature over a defined period of time.

Comparing supervised learning techniques on the task of physical activity recognition.

The objective of this study was to compare the performance of base-level and meta-level classifiers on the task of physical activity recognition. Five wireless kinematic sensors were attached to each subject (n = 25) while they completed a range of basic physical activities in a controlled laboratory setting. Subjects were then asked to carry out similar self-annotated physical activities in a random order and in an unsupervised environment. A combination of time-domain and frequency-domain features were extracted from the sensor data including the first four central moments, zero-crossing rate, average magnitude, sensor cross-correlation, sensor auto-correlation, spectral entropy and dominant frequency components. A reduced feature set was generated using a wrapper subset evaluation technique with a linear forward search and this feature set was employed for classifier comparison. The meta-level classifier AdaBoostM1 with C4.5 Graft as its base-level classifier achieved an overall accuracy of 95%. Equal sized datasets of subject independent data and subject dependent data were used to train this classifier and high recognition rates could be achieved without the need for user specific training. Furthermore, it was found that an accuracy of 88% could be achieved using data from the ankle and wrist sensors only.

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.

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 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.

A classification system for delirium subtyping with the use of a commercial mobility monitor.

The usefulness of motor subtypes of delirium is unclear due to inconsistency in subtyping methods and a lack of validation with objective measures of activity. The activity of 40 patients was measured over 24h with a commercial accelerometer-based activity monitor. Accelerometry data from patients with DSM-IV delirium that were readily divided into hyperactive, hypoactive and mixed motor subtypes, were used to create classification trees that were subsequently applied to the remaining cohort to define motoric subtypes. The classification trees used the periods of sitting/lying, standing, stepping and number of postural transitions as measured by the activity monitor as determining factors from which to classify the delirious cohort. The use of a classification system shows how delirium subtypes can be categorised in relation to overall activity and postural changes, which was one of the most discriminating measures examined. The classification system was also implemented to successfully define other patient motoric subtypes. Motor subtypes of delirium defined by observed ward behaviour differ in electronically measured activity levels.

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.

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.

Accelerometer versus footswitch evaluation of gait unsteadiness and temporal characteristics of gait in two elderly patient groups.

We examined anterior-posterior and medio-lateral head and trunk movements during gait using accelerometers, and a footswitch evaluation of temporal gait parameters of elderly fallers with a primary diagnosis of Orthostatic Hypotension, elderly fallers without Orthostatic Hypotension, and a control group of healthy elderly non-fallers. We wanted to evaluate whether both sets of measures can be used to differentiate between patient groups. We were able to significantly differentiate between the three elderly groups to the same extent using both sets of measures.

A footswitch evaluation of the gait of elderly fallers with and without a diagnosis of orthostatic hypotension and healthy elderly controls.

Gait abnormalities are a recognised risk factor for falling in the elderly and variability in gait has been shown to be a measurable predictor of falls. We carried out a footswitch evaluation of the temporal parameters of gait of elderly fallers with a primary diagnosis of Orthostatic Hypotension, elderly fallers without a diagnosis of Orthostatic Hypotension and a control group of healthy elderly non-fallers. We hypothesized that elderly persons with Orthostatic Hypotension are falling purely as a consequence of their vascular abnormalities and are not falling for the same reasons as regular elderly fallers, including biomechanical irregularities. Therefore it was assumed that their gait pattern would not be similar to that of regular elderly fallers but instead would resemble that of healthy elderly non-fallers. Results show that elderly fallers with or without a diagnosis of Orthostatic Hypotension tend to spend more time in the stance phase of gait, possibly due to a fear of falling. Elderly fallers with a diagnosis of Orthostatic Hypotension have similar levels of gait variability as healthy elderly Controls. These are significantly less than elderly fallers without Orthostatic Hypotension. Therefore elderly fallers with a diagnosis of Orthostatic Hypotension may not be falling for the same reasons as regular elderly fallers.

Development and evaluation of new blood pressure and heart rate signal analysis techniques to assess orthostatic hypotension and its subtypes.

Orthostatic hypotension (OH) is a clinical condition, which frequently results in symptoms such as syncope, dizziness during standing, weakness, blurred vision and fatigue. It is defined as a sustained drop in blood pressure exceeding 20 mmHg systolic or 10 mmHg diastolic occurring within 3 min of assuming upright posture, and is a common causal factor for falls in the elderly. Since 1986, tilt-table testing has become widely used in the diagnosis of OH. The Finometer provides non-invasive monitoring of haemodynamic changes during tilt-table testing. In this study, new algorithms for parameter extraction from Finometer data were developed, with specific reference to the diagnosis of OH. Algorithms were developed to assess the rates of change of haemodynamic variables in response to head-up tilt testing, a previously unexamined aspect of tilt-table testing. These algorithms were applied to the Finometer measurements of 20 patients, who underwent tilt-table testing in the Mid-Western Regional Hospital, Limerick. The data extraction algorithms were shown to accurately record changes in haemodynamic variables for further analysis. It was also demonstrated that the rate of change of blood pressure during the head-up tilt-table testing could have prognostic significance for OH.

Orthostatic hypotension: a new classification system.

AIMS: Orthostatic hypotension (OH) is a common condition, which is defined as a reduction in systolic blood pressure of >or=20 mmHg or diastolic blood pressure of >or=10 mmHg within 3 min of orthostatic stress. Utilizing total peripheral resistance (TPR) and cardiac output (CO) measurements during tilt-table testing (Modelflow method), we classified OH into three categories, namely arteriolar, venular, and mixed. The principle defect in arteriolar OH is impaired vasoconstriction after orthostatic stress, reflected by absence of the compensatory increase in TPR. In venular OH, the predominant defect is excessive reduction in venous return, reflected by a large drop in CO after orthostatic stress with marked tachycardia. Mixed OH is due to a combination of both these mechanisms. METHODS AND RESULTS: We analysed haemodynamic parameters of 110 patients with OH and categorized them as arteriolar, venular, or mixed. Significant differences between the groups were found for the magnitude and time to reach nadir of the systolic blood pressure drop post-head-up tilt. The mixed OH category had the largest systolic blood pressure reduction (42.5, 31.9, 53.3 mmHg, P < 0.001) and the longest nadir time (18.6, 20, 30.7 s, P = 0.002). CONCLUSION: This is a practical classification tool and when validated physiologically, this system could be useful in directing treatment of OH.