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Approximately one-third of people who recover from a stroke require some form of assistance to walk. Repetitive task-oriented rehabilitation interventions have been shown to improve motor control and function in people with stroke. Our long-term goal is to design and test an intensive task-oriented intervention that will utilize the two primary components of constrained-induced movement therapy: massed, task-oriented training and behavioral methods to increase use of the affected limb in the real world. The technological component of the intervention is based on a wearable footwear-based sensor system that monitors relative activity levels, functional utilization, and gait parameters of affected and unaffected lower extremities. The purpose of this study is to describe a methodology to automatically identify temporal gait parameters of poststroke individuals to be used in assessment of functional utilization of the affected lower extremity as a part of behavior enhancing feedback. An algorithm accounting for intersubject variability is capable of achieving estimation error in the range of 2.6–18.6% producing comparable results for healthy and poststroke subjects. The proposed methodology is based on inexpensive and user-friendly technology that will enable research and clinical applications for rehabilitation of people who have experienced a stroke. [ABSTRACT FROM PUBLISHER]

The article discusses the fundamental components of weight regulations in obesity and their relative contribution to the energy equation. It provides the equation for energy gap which is the equilibrium point between the energy expenditure, composed of three components such as the basal metabolic rate, thermic effect of food, and activity energy expenditure, and energy intake. It also outlines the engineering challenge in developing techniques to measure the total energy equation

Regaining the ability to walk is a major rehabilitation goal after a stroke. Recent research suggests that, in people with stroke, task-oriented and intensive rehabilitation strategies can drive cortical reorganization and increase activity levels. This paper describes development and pilot testing of a novel wearable device for Real-Time Gait and Activity Improving Telerehabilitation (RT-GAIT), designed for use with such rehabilitation strategies. The RT-GAIT provides auditory or tactile feedback to the individual wearing the platform. The feedback is based on the amount of time spent in stance phase on each foot, as measured by the pressure sensors embedded into the insoles. The system was initially bench-validated using sensor signals collected in a previous study. Next, a clinical case study was conducted with one post-stroke individual. The results of the case study suggest that the RT-GAIT device can potentially improve the gait parameters. Mean difference in stance times between the healthy limb and paretic limb was improved by 48% and the standard deviation for the same was improved by 87.5%, between baseline measurements and the measurements taken after the treatment with the RT-GAIT.

Measurement of physical activity is increasingly important in health research. We sought to determine the accuracy and sensitivity to non-exercise activity of three activity monitors worn simultaneously by healthy adult women participating in a structured activity protocol. Ten normal-weight women wore the Intelligent Device for Energy Expenditure and Activity (IDEEA), the SmartShoe, and the SenseWear Armband, during activities that included standing, sitting still, sitting and fidgeting, lying down, and walking at varying speeds. Percentage of time postures correctly identified was determined for the IDEEA and the SmartShoe, and activity counts collected from all three devices were compared. Posture was detected with high accuracy by both the IDEEA and the SmartShoe (97.4% and 94.2% accuracy, respectively). The SmartShoe showed superior sensitivity to movement while seated ("fidgeting") compared with the IDEEA (p=0.004 and 0.049 difference between postures, respectively); all three devices distinguished between fast and slow walking. Data support the ability of the IDEEA and the SmartShoe to recognize basic postures in healthy normal-weight women, as well as to detect fidgeting within the seated position.

In our previous research we developed a SmartShoe--a shoe based physical activity monitor that can reliably differentiate between major postures and activities, accurately estimate energy expenditure of individuals, measure temporal gait parameters, and estimate body weights. In this paper we present the development of the next stage of the SmartShoe evolution--SmartStep, a physical activity monitor that is fully integrated into an insole, maximizing convenience and social acceptance of the monitor. Encapsulating the sensors, Bluetooth Low Energy wireless interface and the energy source within an assembly repeatedly loaded with high forces created during ambulation presented new design challenges. In this preliminary study we tested the ability of the SmartStep to measure the pressure differences between static weight-bearing and non-weight-bearing activities (such as no load vs. sitting vs. standing) as well as capture pressure variations during walking. We also measured long-term stability of the sensors and insole assembly under cyclic loading in a mechanical testing system.

Improving community mobility is a common goal for persons with stroke. Measuring daily physical activity is helpful to determine the effectiveness of rehabilitation interventions. In our previous studies, a novel wearable shoe-based sensor system (SmartShoe) was shown to be capable of accurately classify three major postures and activities (sitting, standing, and walking) from individuals with stroke by using Artificial Neural Network (ANN). In this study, we utilized decision tree algorithms to develop individual and group activity classification models for stroke patients. The data was acquired from 12 participants with stroke. For 3-class classification, the average accuracy was 99.1% with individual models and 91.5% with group models. Further, we extended the activities into 8 classes: sitting, standing, walking, cycling, stairs-up, stairs-down, wheel-chair-push, and wheel-chair-propel. The classification accuracy for individual models was 97.9%, and for group model was 80.2%, demonstrating feasibility of multi-class activity recognition by SmartShoe in stroke patients.

The ability to provide real time feedback concerning a person's activity level and energy expenditure can be beneficial for improving activity levels of individuals. Examples include biofeedback systems used for body weight and physical activity management and biofeedback systems for rehabilitation of stroke patients. A critical aspect of any such system is being able to accurately classify data in real-time so that active and timely feedback can be provided. In the paper we demonstrate feasibility of real-time recognition of multiple household and athletic activities on a cell phone using the data collected by a wearable sensor system consisting of SmartShoe sensor and a wrist accelerometer. The experimental data were collected for multiple household and athletic activities performed by a healthy individual. The data was used to train two neural networks, one to be used primarily for sedentary individuals and one for more active individuals. Classification of household activities including ascending stairs, descending stairs, doing the dishes, vacuuming, and folding laundry, achieved 89.62% average accuracy. Classification of athletic activities such as jumping jacks, swing dancing, and ice skating, was performed with 93.13% accuracy. As proof of real-time processing on a mobile platform the trained neural network for healthy individuals was timed and required less than 4 ms to perform each feature vector construction and classification.

Monitoring human beings' major daily activities is important for many biomedical studies. Some monitoring applications may require highly reliable identification of certain postures and activities with desired accuracies well above 99% mark. This paper suggests a method for performing highly accurate classification of postures and activities from data collected by a wearable shoe monitor (SmartShoe) through classification with rejection. The classifier used in this study is support vector machines that uses posterior probability based on the distance of an observation to the separating hyperplane to reject unreliable observations. The results show that a significant improvement (from 95.2% ± 3.5% to 99% ± 1%) of the classification accuracy has been reached after the rejection, as compared to the accuracy reported previously. Such an approach will be especially beneficial in application where high accuracy of recognition is desired while not all observations need to be assigned a class label.

Automatic methods for food intake detection are needed to objectively monitor ingestive behavior of individuals in a free living environment. In this study, a pattern recognition system was developed for detection of food intake through the classification of jaw motion. A total of 7 subjects participated in laboratory experiments that involved several activities of daily living: talking, walking, reading, resting and food intake while being instrumented with a wearable jaw motion sensor. Inclusion of such activities provided a high variability to the sensor signal and thus challenged the classification task. A forward feature selection process decided on the most appropriate set of features to represent the chewing signal. Linear and RBF Support Vector Machine (SVM) classifiers were evaluated to find the most suitable classifier that can generalize the high variability of the input signal. Results showed that an average accuracy of 90.52% can be obtained using Linear SVM with a time resolution of 15 sec.

Obesity prevention and treatment as well as healthy life style recommendation requires the estimation of everyday physical activity. Monitoring posture allocations and activities with sensor systems is an effective method to achieve the goal. However, at present, most devices available rely on multiple sensors distributed on the body, which might be too obtrusive for everyday use. In this study, data was collected from a wearable shoe sensor system (SmartShoe) and a decision tree algorithm was applied for classification with high computational accuracy. The dataset was collected from 9 individual subjects performing 6 different activities--sitting, standing, walking, cycling, and stairs ascent/descent. Statistical features were calculated and the classification with decision tree classifier was performed, after which, advanced boosting algorithm was applied. The computational accuracy is as high as 98.85% without boosting, and 98.90% after boosting. Additionally, the simple tree structure provides a direct approach to simplify the feature set.

The development of accurate and objective tools for monitoring of ingestive behavior (MIB) is one of the most important needs facing studies of obesity and eating disorders. This paper presents the design of an instrumentation module for non-invasive monitoring of food ingestion in laboratory studies. The system can capture signals from a variety of sensors that characterize ingestion process (such as acoustical and other swallowing sensors, strain sensor for chewing detection and self-report buttons). In addition to the sensors, the data collection system integrates time-synchronous video footage that can be used for annotation of subject's activity. Both data and video are simultaneously and synchronously acquired and stored by a LabVIEW-based interface specifically developed for this application. This instrumentation module improves a previously developed system by eliminating the post-processing stage of data synchronization and by reducing the risks of operator's error.

Stroke is the leading cause of disability in the U.S. Many people with stroke have limited walking ability and are inactive. In this paper we describe a novel shoe based sensor, SmartShoe, and a signal processing technique to identify walking activity. The technique was validated with 6 people with walking impairment due to stroke. The results suggest that the SmartShoe is able to accurately identify walking activity. This device could be used to monitor walking activity as well as provide behavioral enhancing feedback to increase activity levels and walking ability in people with stroke for extended periods of time in the real world.

The detection of swallowing events by acoustic means represents an important tool to assess and diagnose swallowing disorders as well as to objectively monitor ingestive behavior of individuals. Acoustic sensors used to register swallowing sounds may also capture sound artifacts arising from intrinsic speech and external noise affecting the detection. In this paper we tested if subsonic frequencies are less prone to artifacts from speech, chewing and other intrinsic sounds than sonic frequencies. A simple method using a throat and an ambient microphone was employed to compare the swallowing detection accuracy by acoustic signals acquired in the sonic (20-2500 Hz) and subsonic (≤ 5 Hz) ranges. Averaged recall values were higher than 85% for both ranges. However, averaged precision values of 50% for subsonic frequencies and of 42% for sonic frequencies were caused by a high number of false positives. These results indicated no significant difference between averaged precision values which may suggest that subsonic frequencies were not less prone to intrinsic sound artifacts than frequencies in the sonic range. Further examination with the addition of a signal classification layer is proposed as a future step to confirm this statement.

Regaining the ability to walk after a stroke is a major rehabilitation goal. Rehabilitation strategies that are task oriented and intensive can drive cortical reorganization and increase activity levels in people after a stroke. This paper describes a novel, wearable device for use with such rehabilitation strategies. The device is based on the combination of a smartphone and in-shoe sensors, and is designed to operate in free living conditions. Data collected from the device can be used for automatic recognition of postures and activities, characterization of extremity use and to provide behavioral enhancing feedback to patients recovering from a stroke. The proposed wearable platform's operation was validated in a small scale study involving three healthy individuals. The average accuracy of classification of three postures and activities was over 99%. Based on the results of validation and previously reported results on recognition of postures and activities in stroke patients, it is anticipated that recognition of postures and activities may be performed with high accuracy in free living conditions.

Stroke is the leading cause of disability in the United States. It is estimated that 700,000 people in the United States will experience a stroke each year and that there are over 5 million Americans living with a stroke. In this paper we describe a novel methodology for automatic recognition of postures and activities in patients with stroke that may be used to provide behavioral enhancing feedback to patients with stroke as part of a rehabilitation program and potentially enhance rehabilitation outcomes. The recognition methodology is based on Support Vector classification of the sensor data provided by a wearable shoe-based device. The proposed methodology was validated in a case study involving an individual with a chronic stroke with impaired motor function of the affected lower extremity and impaired walking ability. The results suggest that recognition of postures and activities may be performed with very high accuracy.