Edward D. Lemaire

Review of fall risk assessment in geriatric populations using inertial sensors

BackgroundFalls are a prevalent issue in the geriatric population and can result in damaging physical and psychological consequences. Fall risk assessment can provide information to enable appropriate interventions for those at risk of falling. Wearable inertial-sensor-based systems can provide quantitative measures indicative of fall risk in the geriatric population.MethodsForty studies that used inertial sensors to evaluate geriatric fall risk were reviewed and pertinent methodological features were extracted; including, sensor placement, derived parameters used to assess fall risk, fall risk classification method, and fall risk classification model outcomes.ResultsInertial sensors were placed only on the lower back in the majority of papers (65%). One hundred and thirty distinct variables were assessed, which were categorized as position and angle (7.7%), angular velocity (11.5%), linear acceleration (20%), spatial (3.8%), temporal (23.1%), energy (3.8%), frequency (15.4%), and other (14.6%). Fallers were classified using retrospective fall history (30%), prospective fall occurrence (15%), and clinical assessment (32.5%), with 22.5% using a combination of retrospective fall occurrence and clinical assessments. Half of the studies derived models for fall risk prediction, which reached high levels of accuracy (62-100%), specificity (35-100%), and sensitivity (55-99%).ConclusionsInertial sensors are promising sensors for fall risk assessment. Future studies should identify fallers using prospective techniques and focus on determining the most promising sensor sites, in conjunction with determination of optimally predictive variables. Further research should also attempt to link predictive variables to specific fall risk factors and investigate disease populations that are at high risk of falls.

Feature Selection for Wearable Smartphone- Based Human Activity Recognition with Able bodied, Elderly, and Stroke Patients

Human activity recognition (HAR), using wearable sensors, is a growing area with the potential to provide valuable information on patient mobility to rehabilitation specialists. Smartphones with accelerometer and gyroscope sensors are a convenient, minimally invasive, and low cost approach for mobility monitoring. HAR systems typically pre-process raw signals, segment the signals, and then extract features to be used in a classifier. Feature selection is a crucial step in the process to reduce potentially large data dimensionality and provide viable parameters to enable activity classification. Most HAR systems are customized to an individual research group, including a unique data set, classes, algorithms, and signal features. These data sets are obtained predominantly from able-bodied participants. In this paper, smartphone accelerometer and gyroscope sensor data were collected from populations that can benefit from human activity recognition: able-bodied, elderly, and stroke patients. Data from a consecutive sequence of 41 mobility tasks (18 different tasks) were collected for a total of 44 participants. Seventy-six signal features were calculated and subsets of these features were selected using three filter-based, classifier-independent, feature selection methods (Relief-F, Correlation-based Feature Selection, Fast Correlation Based Filter). The feature subsets were then evaluated using three generic classifiers (Naïve Bayes, Support Vector Machine, j48 Decision Tree). Common features were identified for all three populations, although the stroke population subset had some differences from both able-bodied and elderly sets. Evaluation with the three classifiers showed that the feature subsets produced similar or better accuracies than classification with the entire feature set. Therefore, since these feature subsets are classifier-independent, they should be useful for developing and improving HAR systems across and within populations.

Wearable Mobility Monitoring Using a Multimedia Smartphone Platform

Understanding mobility is important for effective clinical decision making in the area of physical rehabilitation. Ideally, a persons mobility profile in a nonclinical setting, such as the home or community, could be obtained. This profile would include the environment and context in which the mobility takes place. This paper introduces a novel wearable mobility monitoring system (WMMS) for an objective ubiquitous measurement of mobility. This prototype WMMS was created using a smartphone-based approach that allowed for an all-in-one WMMS. The wearable system is freely worn on a persons belt, such as a normal phone. The WMMS was designed to monitor a users mobility state and to take a photograph when a change of state was detected. These photographs were used to identify the context of mobility events (i.e., using an elevator, walking up/down stairs, and type of walking surface). Validation of the proposed WMMS was performed with five able-bodied subjects performing a structured sequence of mobility tasks. System performance was evaluated by its ability to detect changes of state and the ability to identify context from the photographs. The WMMS demonstrated good potential for community mobility monitoring.

Dynamic gait stability index based on plantar pressures and fuzzy logic

Stability during locomotion, or dynamic stability, is critical to ensure safe locomotion and a high quality of life. A dynamic stability measure should be easily applied in a clinical setting and must provide a quantitative index that can be used for comparisons over a range of tasks and environments. Plantar foot pressure data acquired by shoe-insole sensors have potential to provide such a measure. To generate a quantitative dynamic gait stability index, six gait parameters were extracted from a commercial plantar pressure measurement system (F-Scan): anterior-posterior (A/P) center of force (CoF) motion, medial-lateral (M/L) CoF motion, maximum lateral position, cell triggering, stride time (ST), and double support time (DST). A fuzzy logic controller combined these six parameters and generated the index. To validate the stability index, 15 healthy subjects performed four tasks intended to induce increasing levels of instability. Fifty-seven gait parameter combinations were assessed to determine the most effective index. A combination of A/P motion, M/L motion, maximum lateral position, and cell triggering parameters was the most consistently effective index across all subjects. However, small changes in ST and DST for able-bodied subjects may have reduced the effectiveness of these measures in the index calculation. The index combining all six parameters should be investigated further with populations with disabilities or pathological gait.

Indicators of dynamic stability in transtibial prosthesis users

An improved understanding of factors related to dynamic stability in lower-limb prosthesis users is important, given the high occurrence of falls in this population. Current methods of assessing stability are unable to adequately characterize dynamic stability over a variety of walking conditions. F-Scan Mobile has been used to collect plantar pressure data and six extracted parameters were useful measures of dynamic stability. The aim of this study was to investigate dynamic stability in individuals with unilateral transtibial amputation based on these six parameters. Twenty community ambulators with a unilateral transtibial amputation walked over level ground, uneven ground, stairs, and a ramp while plantar pressure data were collected. For each limb (intact and prosthetic) and condition, six stability parameters related to plantar center-of-pressure perturbations and gait temporal parameters, were computed from the plantar pressure data. Parameter values were compared between limbs, walking condition, and groups (unilateral transtibial prosthesis users and able-bodied subjects). Differences in parameters were found between limbs and conditions, and between prosthesis users and able-bodied individuals. Further research could investigate optimizing parameter calculations for unilateral transtibial prosthesis users and define relationships between potential for falls and the dynamic stability measures.

Design and Evaluation of a Stance-Control Knee-Ankle-Foot Orthosis Knee Joint

Conventional knee-ankle-foot orthoses (KAFOs) are prescribed for people with knee-extensor muscle weakness. However, the orthoses lock the knee in full extension and, therefore, do not permit a natural gait pattern. A new electromechanical stance-control knee-ankle-foot orthosis (SCKAFO) knee joint that employs a novel friction-based belt-clamping mechanism was designed to enable a more natural gait. The SCKAFO knee joint allows free knee motion during swing and other non-weight-bearing activities and inhibits knee flexion while allowing knee extension during weight bearing. A prototype SCKAFO knee joint was mechanically tested to determine the moment at failure, loading behavior, and wear resistance. The mean maximum resisting moment of the SCKAFO knee joint over five loading trials was 69 Nm plusmn4.9 Nm. The SCKAFO knee-joint strength and performance were sufficient to allow testing on a 90 kg subject at normal walking cadence. Proper function of the new electromechanical knee joint was verified in walking trials of an able-bodied subject

Plantar Pressure Parameters for Dynamic Gait Stability Analysis

Dynamic stability measurement is necessary to evaluate human performance over a variety of locomotor environments. In this paper, the suitability of parameters extracted from plantar-pressure measurements as input into a dynamic stability model was investigated. FScan in-shoe pressure data were collected from 15 subjects as they completed four successively more unstable walking tasks. Six parameters met the criteria of being reliably calculated from plantar pressure data, increasing as the task became more unstable, and relating to past measures from the literature: anterior/posterior centre of force (CoF) position, medio-lateral CoF position, double support time, stance time, cell triggering frequency, and maximum lateral CoF position. These parameters could be combined to create an index of dynamic gait stability

Correcting Smartphone orientation for accelerometer-based analysis

A method was developed for rotating a Smartphone accelerometer coordinate system from an offset to a predetermined three-dimensional position to improve accelerometer-based activity identification. A quaternion-based rotation matrix was constructed from an axis-angle pair, produced via algebraic manipulations of the gravity acceleration components in the devices body-fixed frame of reference with the desired position of the vector. The rotation matrix is constructed during quiet standing and then applied to all subsequent accelerometer readings thereafter, transforming their values in this new fixed frame. This method provides a consistent accelerometer orientation between people, thereby reducing Smartphone orientation variability that can adversely affect activity classification algorithms.

Two-degree-of-freedom powered prosthetic wrist.

Prosthetic wrists need to be compact. By minimizing space requirements, a wrist unit can be made for people with long residual limbs. This prosthetic wrist uses two motors arranged across the arm within the envelope of the hand. The drive is transmitted by a differential so that it produces wrist flexion and extension, pronation and supination, or a combination of both. As a case study, it was controlled by a single-prosthesis user with pattern recognition of the myoelectric signals from the forearm. The result is a compact, two-degree-of-freedom prosthetic wrist that has the potential to improve the functionality of any prosthetic hand by creating a hand orientation that more closely matches grasp requirements.

Mobility change-of-state detection using a smartphone-based approach

Understanding the mobility of people with physical disabilities is important for rehabilitation decision making. This paper presents a smartphone-based approach to mobility monitoring. The BlackBerry-based system is clipped to the persons belt. This approach uses an accelerometer signal to identify changes-of-state caused by starting/stopping and postural change. Our finding suggests that a smartphone integrated with an accelerometer could detect changes from static or dynamic movement (i.e., starting to walk, standing still, slowing down), which compares favorably with previous studies using body-fixed accelerometers. This approach is part of the larger framework of Wearable Mobility Monitoring Systems (WMMS).