Accelerometry-based gait analysis and its application to Parkinson's disease assessment--part 1: detection of stride event.

Gait analysis is widely recognized as a promising tool for obtaining objective information on the walking behavior of Parkinson's disease (PD) patients. It is especially useful in clinical practices if gait properties can be captured with minimal instrumentation that does not interfere with the subject's usual behavioral pattern under ambulatory conditions. In this study, we propose a new gait analysis system based on a trunk-mounted acceleration sensor and automatic gait detection algorithm. The algorithm identifies the acceleration signal with high intensity, periodicity, and biphasicity as a possible gait sequence, from which gait peaks due to stride events are extracted by utilizing the cross-correlation and anisotropy properties of the signal. A total of 11 healthy subjects and 12 PD patients were tested to evaluate the performance of the algorithm. The result indicates that gait peaks can be detected with an accuracy of more than 94%. The proposed method may serve as a practical component in the accelerometry-based assessment of daily gait characteristics.

Accelerometry-based gait analysis and its application to Parkinson's disease assessment- part 2: a new measure for quantifying walking behavior.

Gait analysis is a valuable tool for obtaining quantitative information on motor deficits in Parkinson's disease (PD). Since the characteristic gait patterns of PD patients may not be fully identified by brief examination in a clinic, long-term, and unobtrusive monitoring of their activities is essential, especially in a nonclinical setting. This paper describes a single accelerometer-based gait analysis system for the assessment of ambulatory gait properties. Acceleration data were recorded continuously for up to 24 h from normal and PD subjects, from which gait peaks were picked out and the relationship between gait cycle and vertical gait acceleration was evaluated. By fitting a model equation to the relationships, a quantitative index was obtained for characterizing the subjects' walking behavior. The averaged index for PD patients with gait disorder was statistically smaller than the value for normal subjects. The proposed method could be used to evaluate daily gait characteristics and thus contribute to a more refined diagnosis and treatment of the disease.

Development of unconstrained heartbeat and respiration measurement system with pneumatic flow.

The management of health through daily monitoring of heartbeat and respiration signals is of major importance for early diagnosis to prevent diseases of the respiratory and circulatory system. However, such daily health monitoring is possible only if the monitoring system is physically and psychologically noninvasive. In this paper, an unconstrained method of measuring heartbeat and respiration signals, by using a thermistor to measure the air flows from the air mattress to an air tube accompanying the subject's heartbeat and respiration, is proposed. The SN ratio with interference by opening and closing of a door as environmental noise was compared with that obtained by the conventional condenser microphone method. As a result, the SN ratios with the condenser microphone method were 26.6 ± 4.2 dB for heartbeat and 27.8 ± 3.0 dB for respiration, whereas with the proposed method they were 34.9 ± 3.1 dB and 42.1 ± 2.5 dB, respectively.

Unconstrained estimation method of delta-wave percentage included in EEG of sleeping subjects.

This paper describes an unconstrained pneumatic method of estimating the δ-wave activity of the brain from the heartbeat signal. Based on experiments showing that transinformation of the heartbeat signal corrupted by body movement was correlated with the δ-wave activity, we developed a method of estimating the percentage of the δ-wave included in the EEG from the transinformation. The comb filtering technique was used to obtain accurate transinformation. We applied the proposed method to young normal subjects to evaluate the method. As a result, the correlation between the δ -wave included in the EEG and the transinformation was 0.727 and the average error of the estimates of δ-wave percentage was 14.9%. The δ-wave activity and heartbeat activity were shown to be quantitatively related. This suggests that sleep depth can be estimated from the δ -wave percentage estimated by unconstrained measurement of the heartbeat signal of young normal subjects.

Sleep-states-transition model by body movement and estimation of sleep-stage-appearance probabilities by Kalman filter.

The judgment standards of R-K method include ambiguities and are thus compensated by subjective interpretations of sleep-stage scorers. This paper presents a novel method to compensate uncertainties in judgments by the subjective interpretations by the sleep-model estimation approach and by describing the judgments in probabilistic terms. Kalman filter based on the two sleep models with no body movement and with body movement was designed. Sleep stages judged by three different scorers were rejudged by the filter. The two sleep models were stochastically estimated from biosignals from 15 nights' data and the rejudged scores by the filter were evaluated by the data from 5 nights. The average values of kappa statistics, which show the degree of agreement, were 0.85, 0.89, and 0.81, respectively, for the original sleep stages. Because the new method provides probabilities on how surely the sleep belongs to each sleep stage, we were able to determine the most, second most, and third most probable sleep stage. The kappa statistics between the most probable sleep stages were improved to 0.90, 0.93, and 0.84, respectively. Those of sleep stages determined from the most and second most probable were 0.92, 0.94, and 0.89 and those from the most, second most, and third most probable were 0.95, 0.97, and 0.92. The sleep stages estimated by the filter are expressed by probabilistic manner, which are more reasonable in expression than those given by deterministic manner. The expression could compensate the uncertainties in each judgments and thus were more accurate than the direct judgments.

Construction of age model for the evaluation of swallowing function using photo sensors.

For the realization of better in-home nursing environment, understanding the swallowing function and its process are quite essential to realize appropriate rehabilitation policies and dietary menus to prevent aspirations. However, the swallowing function is currently examined using mainly large-scale and expensive methods such as video fluoroscopic examination of swallowing, mesophagia fiber, palatal pressure measurement, CT, and cine MRI, which are difficult to be used at patients' homes. This paper proposes an age model, which applies a self organization map (SOM) to evaluate the swallowing function. As parameters to make the SOM, the lead time, the geniohyoid muscle, and the thyroid muscle are measured using photo sensors with a little invasiveness. To verify the effectiveness of our swallowing function map, the age of the subject determined by the map is compared with his/her actual age. As a result of the comparison, the root mean square error was 6.28 years.

Safe patient transfer system with monitoring of location and vital signs.

In recent years, ubiquitous computing technologies have been applied in the field of medicine. Especially radio frequency identification (RFID) and small sensor networks could provide information about medical practices and patient status in order to prevent malpractices and improve the quality of medical care. As an example of this application, we developed a new system, named "a smart stretcher," which continuously monitors the patient's vital signs and detects apnea during transfer within a hospital. This system consists of a small air-mat type pressure sensor measuring both heart rate and respiration rate and a wireless network transmitting these vital data as well as patient ID to an alerting system to notify hospital staff of patient emergencies. Results of experiments in a clinical setting indicated that the system was reliable in continuous respiration monitoring and detection of apnea during patient transfer on the stretcher; however, detection of heartbeat rate was practically difficult because of the motion noises. Moreover patient ID and location were also correctly detected in real time. These results suggested the feasibility of our system for real clinical use.

Noninvasive measurement of heartbeat, respiration, snoring and body movements of a subject in bed via a pneumatic method.

We have developed a noninvasive pneumatics-based system by which to measure heartbeat, respiration, snoring, and body movements of a subject in bed. A thin, air-sealed cushion is placed under the bed mattress of the subject and the small movements attributable to human automatic vital functions are measured as changes in pressure using a pressure sensor having an almost flat frequency response from 0.1 to 5 kHz and a sensitivity of 56 mV/Pa. Using the newly developed system, heartbeat, respiration, apnea, snoring and body movements are clearly measured. In addition, the optimal signal-to-noise (S/N) ratio by which to evaluate the reliability of the heart rate measurement is presented. Heart rates were measured for four different body postures, 13 different subjects, four different bed mattresses, and three different sensor positions. For these measurements, the S/N ratios ranged from 15.9 to 23.5 dB, and so were determined to be reliable.

Noncontact method for sleep stage estimation.

This paper describes a novel method to estimate sleep stage through noninvasive and unrestrained means. The Rechtschaffen and Kales (R-K) method is a standard to estimate sleep stage. However, it involves restraining the examinee and, thus, induces psychological stress. Furthermore, it requires specialists with a high degree of technical expertise and the use of an expensive polygraph. The sleep estimation method presented here is based on the noninvasive and unrestrained pneumatic biomeasurement method presented by the authors. Sleep stage transition in overnight sleep and the relationship between sleep stage and biosignals measured using the pneumatic method was analyzed and from the results, a mathematical model of sleep was created. Based on this model, a sleep stage estimator, including a sleep stage classifier and observer, was designed. The sleep state transition equation was the basis for the design of this observer, while the observed relationships were the basis for designing a classifier. Agreement of the estimated sleep stages with those obtained using the R-K method for the non-REM stage was 82.6%, for the REM stage was 38.3 % and for Wake was 70.5 %, including disagreement. However, the new method might ultimately result in better estimation of sleep stage due to the fact that it does not physically restrain the patient and does not induce psychological stress.