Lifecorder: a new device for the long-term monitoring of motor activities for Parkinson's disease

The relationships between three-axis accelerometer measures of physical activity and motor symptoms in patients with Parkinson's disease: a single-center pilot study

Background

Various wearable devices for objectively evaluating motor symptoms of patients with Parkinson’s disease (PD) have been developed. Importantly, previous studies have suggested protective effects of physical activity in PD. However, the relationships between conventional clinical ratings for PD and three-axis accelerometer measures of physical activity (e.g., daily physical activity levels [PAL] or metabolic equivalents of task [METs]) are still unclear, particularly for METs. In the current study, we sought to elucidate these relationships on a daily basis, and to clarify optimal predictors for clinical states on a 30-min basis.

Methods

Patients who were hospitalized for adjustment of drugs or deep brain stimulation were enrolled. Using waist-worn three-axis accelerometers, PAL and METs parameter data were obtained and compared with UPDRS-3[On] and symptom diary data. We extracted data from the patients’ best and worst days, defined by the best and worst UPDRS-3[On] scores, respectively. Thus, 22 data sets from 11 patients were extracted. We examined the correlations and produced scatter plots to represent the relationships, then investigated which METs parameters and activity patterns were the best predictors for “On” and “dyskinesia”.

Results

The parameter “mean METs value within the 95–92.5 percentile range on a day (95–92.5 percentile value)” exhibited the strongest correlation with conventional daily clinical ratings (Rho: − 0.799 for UPDRS-3[On], 0.803 for On hours [p < 0.001]). Scatter plots suggested that PAL tended to have higher values in patients with involuntary movement. However, METs parameters focusing on higher METs seemed to alleviate this tendency. We clarified that “time over 2.0 METs” and “time over 1.5 METs” could be predictors for “On” and “dyskinesia” on a 30-min basis, respectively (AUROC: 0.779 and 0.959, 95% CI: 0.733–0.824 and 0.918–1.000). The specificity and sensitivity of the optimal activity pattern for “On” were 0.858 and 0.621.

Conclusions

This study suggested feasible activity patterns and METs parameters for objective evaluation of motor symptoms on a 30-min or daily basis. Three-axis accelerometer measures focusing on higher METs may be appropriate for evaluating physical activity. Further larger-scale studies are necessary to clarify the validity, reliability, and clinical utility of these objective measures.

Data-Driven Based Approach to Aid Parkinson's Disease Diagnosis

This article presents a machine learning methodology for diagnosing Parkinson's disease (PD) based on the use of vertical Ground Reaction Forces (vGRFs) data collected from the gait cycle. A classification engine assigns subjects to healthy or Parkinsonian classes. The diagnosis process involves four steps: data pre-processing, feature extraction and selection, data classification and performance evaluation. The selected features are used as inputs of each classifier. Feature selection is achieved through a wrapper approach established using the random forest algorithm. The proposed methodology uses both supervised classification methods including K-nearest neighbour (K-NN), decision tree (DT), random forest (RF), Naïve Bayes (NB), support vector machine (SVM) and unsupervised classification methods such as K-means and the Gaussian mixture model (GMM). To evaluate the effectiveness of the proposed methodology, an online dataset collected within three different studies is used. This data set includes vGRF measurements collected from eight force sensors placed under each foot of the subjects. Ninety-three patients suffering from Parkinson's disease and 72 healthy subjects participated in the experiments. The obtained performances are compared with respect to various metrics including accuracy, precision, recall and F-measure. The classification performance evaluation is performed using the leave-one-out cross validation. The results demonstrate the ability of the proposed methodology to accurately differentiate between PD subjects and healthy subjects. For the purpose of validation, the proposed methodology is also evaluated with an additional dataset including subjects with neurodegenerative diseases (Amyotrophic Lateral Sclerosis (ALS) and Huntington's disease (HD)). The obtained results show the effectiveness of the proposed methodology to discriminate PD subjects from subjects with other neurodegenerative diseases with a relatively high accuracy.

Segmentation of Gait Sequences in Sensor-Based Movement Analysis: A Comparison of Methods in Parkinson's Disease

Robust gait segmentation is the basis for mobile gait analysis. A range of methods have been applied and evaluated for gait segmentation of healthy and pathological gait bouts. However, a unified evaluation of gait segmentation methods in Parkinson’s disease (PD) is missing. In this paper, we compare four prevalent gait segmentation methods in order to reveal their strengths and drawbacks in gait processing. We considered peak detection from event-based methods, two variations of dynamic time warping from template matching methods, and hierarchical hidden Markov models (hHMMs) from machine learning methods. To evaluate the methods, we included two supervised and instrumented gait tests that are widely used in the examination of Parkinsonian gait. In the first experiment, a sequence of strides from instructed straight walks was measured from 10 PD patients. In the second experiment, a more heterogeneous assessment paradigm was used from an additional 34 PD patients, including straight walks and turning strides as well as non-stride movements. The goal of the latter experiment was to evaluate the methods in challenging situations including turning strides and non-stride movements. Results showed no significant difference between the methods for the first scenario, in which all methods achieved an almost 100% accuracy in terms of F-score. Hence, we concluded that in the case of a predefined and homogeneous sequence of strides, all methods can be applied equally. However, in the second experiment the difference between methods became evident, with the hHMM obtaining a 96% F-score and significantly outperforming the other methods. The hHMM also proved promising in distinguishing between strides and non-stride movements, which is critical for clinical gait analysis. Our results indicate that both the instrumented test procedure and the required stride segmentation algorithm have to be selected adequately in order to support and complement classical clinical examination by sensor-based movement assessment.

The Natural Progression of Parkinson's Disease in a Small Cohort with 15 Drug-naïve Patients

BACKGROUND

The studies of the natural progression of Parkinson's disease (PD) in Chinese populations have been lacking. To address this issue and obtain a preliminary data, we conducted a PD progression assessment in 15 adults with de novo PD from a nutritional intervention trial (NIT) cohort in Lin County China.

METHODS

Using the Copiah County screening questionnaire and United Kingdom Parkinson's Disease Society Brain Bank diagnostic criteria, we surveyed the available NIT cohort members in 2000 and diagnosed 86 patients as PD. In 2010, we resurveyed all PD patients and confirmed definite PD diagnosis in 15 cases with the rest of them being dead (54); having probable (10) PD or vascular Parkinsonism (3); refusing to participate (2); or being away (2). In both surveys, we used Hoehn and Yahr (HY) scale and assessed the disease progression. Unified Parkinson's Disease Rating Scale (UPDRS) was added to the second survey.

RESULTS

In 2010, the average disease duration for 15 definite PD patients was 13.6 ± 7.3 years. Over a 10-year time span, 9 out of 15 patients remained at the same HY stage while the remaining 6 progressed. Rigidity (47% vs. 100%; P = 0.002) and postural instability (7% vs. 47%; P = 0.005) worsened significantly. The mean UPDRS motor scores in 2010 were 39.4 ± 23.7.

CONCLUSIONS

Overall worsening of motor function in PD seems to be the rule in this untreated cohort, and their rate of progression seemed to be slower than those reported in the western populations.

How far do the complaints of patients with Parkinson's disease reflect motor fluctuation? Quantitative analysis using a portable gait rhythmogram

In advanced-stage Parkinson's disease (PD), motor fluctuation is a frequent and disabling problem. Assessment of motor fluctuation depends on patient's subjective self-statement. We examined whether the subjective fluctuation matched the objective motor fluctuation defined by gait disorders. Using a new device, the portable gait rhythmogram, we recorded gait cadence and acceleration continuously over the 24-hour period in 54 patients with PD and 17 normal controls, for the quantitative evaluation of motor fluctuation. The patients were asked to estimate motor fluctuation every hour. In 44 of 54 patients, changes in the cadence were associated with simultaneous changes in acceleration. We examined the subjective fluctuation in these 44 patients who were confirmed to have motor fluctuation. Nineteen (82.7%) of 23 patients who felt no fluctuation showed distinct gait disorders. During off time, they walked with marked short or bradykinetic stepping. No matching changes were observed in either the cadence or acceleration in 11 (52.4%) of 21 patients who perceived motor fluctuation. No synchronization was noted in 30 (68.2%) of the 44 patients, between the times of subjectively assessed motor fluctuation and those of quantitative analysis of gait disorder. This discrepancy suggests that the objective continuous recording of the cadence and acceleration is necessary to understand motor fluctuation.

Deficits in scaling of gait force and cycle in parkinsonian gait identified by long-term monitoring of acceleration with the portable gait rhythmogram

To examine the range of gait acceleration and cycle in daily walking of patients with Parkinson's disease (PD), we compared the gait of 40 patients with PD and 17 normal controls by using a newly developed long-term monitoring device that extracts gait-related accelerations from overall movements-related accelerations. The range of change in gait acceleration, relative to the control, was less than 75% in 12 patients. The range of change in gait cycle was less than 75% in 8 patients. The range of changes in both parameters was less than 75% in 4 patients. The results suggest narrow changes in gait parameters in PD.

Validation of 24-hour ambulatory gait assessment in Parkinson's disease with simultaneous video observation

Background

Parkinson's disease (PD) is a neurodegenerative disorder resulting in motor disturbances that can impact normal gait. Although PD initially responds well to pharmacological treatment, as the disease progresses efficacy often fluctuates over the course of the day, and clinical management would benefit from long-term objective measures of gait. We have previously described a small device worn on the shank that uses acceleration and angular velocity sensors to calculate stride length and identify freezing of gait in PD patients. In this study we extend validation of the gait monitor to 24-h using simultaneous video observation of PD patients.

Methods

A sleep laboratory was adapted to perform 24-hr video monitoring of patients while wearing the device. Continuous video monitoring of a sleep lab, hallway, kitchen and conference room was performed using a 4-camera security system and recorded to hard disk. Subjects (3) wore the gait monitor on the left shank (just above the ankle) for a 24-h period beginning around 5 pm in the evening. Accuracy of stride length measures were assessed at the beginning and end of the 24-h epoch. Two independent observers rated the video logs to identify when subjects were walking or lying down.

Results

The mean error in stride length at the start of recording was 0.05 m (SD 0) and at the conclusion of the 24 h epoch was 0.06 m (SD 0.026). There was full agreement between observer coding of the video logs and the output from the gait monitor software; that is, for every video observation of the subject walking there was a corresponding pulse in the monitor data that indicated gait.

Conclusions

The accuracy of ambulatory stride length measurement was maintained over the 24-h period, and there was 100% agreement between the autonomous detection of locomotion by the gait monitor and video observation.

The usefulness of an accelerometer for monitoring total energy expenditure and its clinical application for predicting body weight changes in type 2 diabetic korean women

BACKGROUND

The purpose of this study was to evaluate the usefulness of an accelerometer in predicting body weight (BW) change during a lifestyle intervention and to find out whether exercise or overall physical activity is associated with change in insulin sensitivity and body composition.

METHODS

A total of 49 overweight (body mass index [BMI] ≥ 23 kg/m(2)) women with diabetes were enrolled and performed lifestyle intervention while monitoring BW, total energy expenditure (TEE) and physical activity energy expenditure (PAEE) using an accelerometer, and energy intake (EI) using a three-day dietary record at baseline and every 2 weeks for 12 weeks. We assessed body composition using bioimpedance analysis and compared the actual BW change to the predicted BW change, which was calculated from the energy deficit (ED) between EI and TEE (ED = EI-TEE).

RESULTS

Mean age was 57.2 years, duration of diabetes was 8.0 years, and BMI was 27.8 kg/m(2). There was no significant difference between EI and TEE at baseline. For 12 weeks, the ED was 474.0 kcal·day(-1), which was significantly correlated with BW change (-3.1 kg) (r = 0.725, P < 0.001). However, the actual BW change was 50% lower than the predicted BW change. Both TEE and PAEE correlated with change in K(ITT) (r = 0.334, P = 0.019; r = 0.358, P = 0.012, respectively), BMI (r = -0.395, P = 0.005; r = -0.347, P = 0.015, respectively), and fat mass (r = -0.383, P = 0.007; r = -0.395, P = 0.005, respectively), but only TEE correlated with fat free mass change (r = -0.314, P = 0.030).

CONCLUSION

The accelerometer appears to be a useful tool for measuring TEE under free-living conditions for both short- and long-term periods.

24-hour recording of parkinsonian gait using a portable gait rhythmogram

OBJECTIVE

In the advanced stage of Parkinson's disease (PD), motor fluctuation is a frequent and a disabling problem. Despite its importance, motor fluctuation has received little scientific analysis probably due to limitation in objective assessment. Here, we focused on gait disorders to estimate motor fluctuation in daily activities.

PATIENTS AND METHODS

Using a new device, the portable gait rhythmogram, we recorded gait rhythm continuously over 24 hours in 22 patients with PD and in 11 normal controls, for quantitative evaluation of motor fluctuation. The duration of one gait cycle was measured.

RESULTS

Continuous 24-hour recording identified changes in gait rhythm, which correlated with fluctuation of PD symptoms. Different motor fluctuations were observed; a shift to a faster gait cycle was noted in patients with short-step walking, festination or freezing of gait, whereas a shift to a slower gait cycle was observed in patients with bradykinesia or instability.

CONCLUSION

Characterization of motor fluctuation using this device could help in the selection of appropriate anti-PD medications.

Comparison of Lifecorder EX and ActiGraph accelerometers under free-living conditions

The Kenz Lifecorder EX (LC; Suzuken Co. Ltd., Nagoya, Japan) offers several potentially attractive features for researchers and practitioners compared with accelerometers such as the ActiGraph (AG; ActiGraph Health Services, Fort Walton Beach, Fla.). The purposes of this study were (i) to evaluate the LC's intra-model reliability for outputs of steps and time spent in moderate, vigorous, and combined moderate plus vigorous physical activity (MVPA) and (ii) to compare the same LC vs. AG outputs under free-living conditions. Ten participants (n = 5 males) wore two LCs and one AG accelerometer during all waking hours on one day. Steps were outputted from all monitors. Additionally, two LC and five AG intensity derivations were used to assess time in moderate activity, vigorous activity, and MVPA. Intra-class correlations (ICC) were used to assess intra-model reliability between LCs. Paired t tests and repeated-measures analyses of variance (ANOVAs) were used to assess differences between the two LCs and LC vs. AG outputs of steps and time in various intensity derivations where appropriate. No significant differences were detected between outputs from different LCs (ICCs ranged from 0.95 to 0.99). The LC detected significantly fewer steps vs. AG (mean difference = 1516 steps). All LC vs. AG vigorous-intensity derivations provided similar outputs. Additionally, comparable estimates of MVPA time were produced by one of two LC intensity derivations compared with specific AG cut points established each by Freedson, Hendelman (walking), and Matthews. LC displayed high inter-model reliability. Although the LC detected fewer steps than the AG, the LC detects time in specific PA intensity categories comparable to several existing AG cut points.

Long-term monitoring of gait in Parkinson's disease

A new system for long-term monitoring of gait in Parkinson's disease (PD) has been developed and validated. The characteristics of every stride taken over 10-h epochs were acquired using a lightweight ankle-mounted sensor array that transmitted data wirelessly to a small pocket PC at a rate of 100 Hz. Stride was calculated from the vertical linear acceleration and pitch angular velocity of the leg with an accuracy of 5 cm. Results from PD patients (5) demonstrate the effectiveness of long-term monitoring of gait in a natural environment. The small, variable stride length characteristic of Parkinsonian gait, and fluctuations of efficacy associated with levodopa therapy, such as delayed onset, wearing off, and the 'off/on' effect, could reliably be detected from long-term changes in stride length.

Feasibility study of the integrated circuit tag monitoring system for dementia residents in Japan

Videotaping, direct observation, and biomechanical devices have been used to measure the wandering behavior of institutionalized people with dementia and to identify patterns of movement. Owing to technical limitations, the variability in movement patterns or the distance traveled could not be examined. The present study examined the feasibility of an integrated circuit tag monitoring system to monitor the movement patterns of Japanese dementia residents. This system generated the following data: the frequency of detection by each receiver, the duration of stay in each location, the cumulative distance walked per day, and the graphic display of the movement pattern. This new system offers objective measurements of ambulation in time and space, which can be used to characterize demented patients and to evaluate the effects of treatment and care.