Low-power sensor module for long-term activity monitoring

Direct Memory Access-Based Data Storage for Long-Term Acquisition Using Wearables in an Energy-Efficient Manner

This study introduces a lightweight storage system for wearable devices, aiming to optimize energy efficiency in long-term and continuous monitoring applications. Utilizing Direct Memory Access and the Serial Peripheral Interface protocol, the system ensures efficient data transfer, significantly reduces energy consumption, and enhances the device autonomy. Data organization into Time Block Data (TBD) units, rather than files, significantly diminishes control overhead, facilitating the streamlined management of periodic data recordings in wearable devices. A comparative analysis revealed marked improvements in energy efficiency and write speed over existing file systems, validating the proposed system as an effective solution for boosting wearable device performance in health monitoring and various long-term data acquisition scenarios.

Turning in Circles: Understanding Manual Wheelchair Use Towards Developing User-Friendly Steering Systems

For people with physical disabilities, manual wheelchairs are essential enablers of mobility, participation in society, and a healthy lifestyle. Their most general design offers great flexibility and direct feedback, but has been described to be inefficient and demands good coordination of the upper extremities while critically influencing users’ actions. Multiple research groups have used Inertial Measurement Units (IMUs) to quantify physical activities in wheelchairs arguing that knowledge over behavioural patterns in manual wheelchair usage can guide technological development and improved designs. The present study investigates turning behaviour among fulltime wheelchair users, laying the foundation of the development of novel steering systems that allow directing kinetic energy by means other than braking. Three wearable sensors were installed on the wheelchairs of 14 individuals for tracking movement over an entire week. During detected “moving windows”, phases where the velocities of the two rear wheels differed by more than 0.05 m/s were considered as turns. Kinematic characteristics for both turns-on-the-spot as well as for moving turns were then derived from the previously reconstructed wheeled path. For the grand total of 334 km of recorded wheelchair movement, a turn was detected every 3.6 m, which equates to about 900 turns per day on average and shows that changing and adjusting direction is fundamental in wheelchair practice. For moving turns, a median turning radius of 1.09 m and a median turning angle of 39° were found. With a median of 89°, typical turning angles were considerably larger for turns-on-the-spot, which accounted for roughly a quarter of the recognised turns and often started from a standstill. These results suggest that a frequent pattern in daily wheelchair usage is to initiate movement with an orienting turn-on-the-spot, and cover distances with short, straightforward sections while adjusting direction in small and tight moving turns. As large bends often require simultaneous pushing and breaking, this is, perhaps, the result of users intuitively optimising energy efficiency, but more research is needed to understand how the design of the assistive devices implicitly directs users’ movement.

Systematic review on the application of wearable inertial sensors to quantify everyday life motor activity in people with mobility impairments

BACKGROUND

Recent advances in wearable sensor technologies enable objective and long-term monitoring of motor activities in a patient's habitual environment. People with mobility impairments require appropriate data processing algorithms that deal with their altered movement patterns and determine clinically meaningful outcome measures. Over the years, a large variety of algorithms have been published and this review provides an overview of their outcome measures, the concepts of the algorithms, the type and placement of required sensors as well as the investigated patient populations and measurement properties.

METHODS

A systematic search was conducted in MEDLINE, EMBASE, and SCOPUS in October 2019. The search strategy was designed to identify studies that (1) involved people with mobility impairments, (2) used wearable inertial sensors, (3) provided a description of the underlying algorithm, and (4) quantified an aspect of everyday life motor activity. The two review authors independently screened the search hits for eligibility and conducted the data extraction for the narrative review.

RESULTS

Ninety-five studies were included in this review. They covered a large variety of outcome measures and algorithms which can be grouped into four categories: (1) maintaining and changing a body position, (2) walking and moving, (3) moving around using a wheelchair, and (4) activities that involve the upper extremity. The validity or reproducibility of these outcomes measures was investigated in fourteen different patient populations. Most of the studies evaluated the algorithm's accuracy to detect certain activities in unlabeled raw data. The type and placement of required sensor technologies depends on the activity and outcome measure and are thoroughly described in this review. The usability of the applied sensor setups was rarely reported.

CONCLUSION

This systematic review provides a comprehensive overview of applications of wearable inertial sensors to quantify everyday life motor activity in people with mobility impairments. It summarizes the state-of-the-art, it provides quick access to the relevant literature, and it enables the identification of gaps for the evaluation of existing and the development of new algorithms.

Tremor analysis with wearable sensors correlates with outcome after thalamic deep brain stimulation

INTRODUCTION

Thalamic deep brain stimulation (DBS) provides excellent tremor control in most patients with essential tremor (ET). However, not all tremor patients show clinically significant improvement after DBS surgery. Currently, there is no reliable clinical or instrument-based measure to predict how patients respond to DBS. Therefore, we set out to provide a method for tremor outcome prediction prior to surgery.

METHODS

We retrospectively analysed quantitative tremor data collected with inertial measurement units (IMU) in 13 patients who underwent DBS surgery in the ventral intermediate nucleus of the thalamus (VIM). All patients were diagnosed with either ET or ET-plus according to current diagnostic criteria of the movement disorder society. We used linear and logistic regression models to evaluate the influence of different tremor characteristics on tremor outcome.

RESULTS

We found that the ratio between the amplitude of the first overtone and the amplitude of the fundamental frequency, denoted as the Harmonic Index, has a significant influence on tremor reduction after DBS surgery. This measure shows a strong correlation with the post-operative improvement of tremor outcome based on the Whiget Tremor Rating Scale.

CONCLUSION

Based on these findings, we propose a novel approach to predict tremor outcome after DBS surgery. Quantitative tremor assessment adds to the preoperative prediction of DBS response and might therefore have a relevant clinical impact in the management of patients suffering from pharmacoresistant tremor.

Wearable technology in stroke rehabilitation: towards improved diagnosis and treatment of upper-limb motor impairment

Stroke is one of the main causes of long-term disability worldwide, placing a large burden on individuals and society. Rehabilitation after stroke consists of an iterative process involving assessments and specialized training, aspects often constrained by limited resources of healthcare centers. Wearable technology has the potential to objectively assess and monitor patients inside and outside clinical environments, enabling a more detailed evaluation of the impairment and allowing the individualization of rehabilitation therapies. The present review aims to provide an overview of wearable sensors used in stroke rehabilitation research, with a particular focus on the upper extremity. We summarize results obtained by current research using a variety of wearable sensors and use them to critically discuss challenges and opportunities in the ongoing effort towards reliable and accessible tools for stroke rehabilitation. Finally, suggestions concerning data acquisition and processing to guide future studies performed by clinicians and engineers alike are provided.

Predicting upper limb compensation during prehension tasks in tetraplegic spinal cord injured patients using a single wearable sensor

Upper limb (UL) compensation is a common strategy of patients with a high spinal cord injury (SCI), i.e., tetraplegic patients, to perform activities of daily living (ADLs) despite their sensorimotor deficits. Currently, an objective and sensitive tool to assess UL compensation, which is applicable in the clinical routine and in the daily life of patients, is missing. In this work, we propose a metric to quantify this compensation using a single inertial measurement unit (IMU). The spread of forearm pitch angles of an IMU attached to the wrist of 17 SCI patients and 18 healthy controls performing six prehension tasks of the graded redefined assessment of strength, sensibility and prehension (GRASSP) was extracted. Using the spread of the forearm pitch angles, a classification of UL compensation was possible with very good to excellent accuracies in all six different prehension tasks. Furthermore, the spread of forearm pitch angles correlated moderately to very strongly with qualitative and quantitative GRASSP prehension scores and the task duration. Therefore, we conclude that our proposed method has a high potential to classify compensation accurately and objectively and might be used to quantify the degree of UL compensation in ADLs. Thus, this method could be implemented in clinical trials investigating the effectiveness of interventions targeting UL functions.

Reliability of Wearable-Sensor-Derived Measures of Physical Activity in Wheelchair-Dependent Spinal Cord Injured Patients

Physical activity (PA) has been shown to have a positive influence on functional recovery in patients after a spinal cord injury (SCI). Hence, it can act as a confounder in clinical intervention studies. Wearable sensors are used to quantify PA in various neurological conditions. However, there is a lack of knowledge about the inter-day reliability of PA measures. The objective of this study was to investigate the single-day reliability of various PA measures in patients with a SCI and to propose recommendations on how many days of PA measurements are required to obtain reliable results. For this, PA of 63 wheelchair-dependent patients with a SCI were measured using wearable sensors. Patients of all age ranges (49.3 ± 16.6 years) and levels of injury (from C1 to L2, ASIA A-D) were included for this study and assessed at three to four different time periods during inpatient rehabilitation (2 weeks, 1 month, 3 months, and if applicable 6 months after injury) and after in-patient rehabilitation in their home-environment (at least 6 months after injury). The metrics of interest were total activity counts, PA intensity levels, metrics of wheeling quantity and metrics of movement quality. Activity counts showed consistently high single-day reliabilities, while measures of PA intensity levels considerably varied depending on the rehabilitation progress. Single-day reliabilities of metrics of movement quantity decreased with rehabilitation progress, while metrics of movement quality increased. To achieve a mean reliability of 0.8, we found that three continuous recording days are required for out-patients, and 2 days for in-patients. Furthermore, the results show similar weekday and weekend wheeling activity for in- and out-patients. To our knowledge, this is the first study to investigate the reliability of an extended set of sensor-based measures of PA in both acute and chronic wheelchair-dependent SCI patients. The results provide recommendations for sensor-based assessments of PA in clinical SCI studies.

Estimation of Energy Expenditure in Wheelchair-Bound Spinal Cord Injured Individuals Using Inertial Measurement Units

A healthy lifestyle reduces the risk of cardio-vascular disease. As wheelchair-bound individuals with spinal cord injury (SCI) are challenged in their activities, promoting and coaching an active lifestyle is especially relevant. Although there are many commercial activity trackers available for the able-bodied population, including those providing feedback about energy expenditure (EE), activity trackers for the SCI population are largely lacking, or are limited to a small set of activities performed in controlled settings. The aims of the present study were to develop and validate an algorithm based on inertial measurement unit (IMU) data to continuously monitor EE in wheelchair-bound individuals with a SCI, and to establish reference activity values for a healthy lifestyle in this population. For this purpose, EE was measured in 30 subjects each wearing four IMUs during 12 different physical activities, randomly selected from a list of 24 activities of daily living. The proposed algorithm consists of three parts: resting EE estimation based on multi-linear regression, an activity classification using a k-nearest-neighbors algorithm, and EE estimation based on artificial neural networks (ANNs). The mean absolute estimation error for the ANN-based algorithm was 14.4% compared to indirect calorimeter measurements. Based on reference values from the literature and the data collected within this study, we recommend wheeling 3 km per day for a healthy lifestyle in wheelchair-bound SCI individuals. Combining the proposed algorithm with a recommendation for physical activity provides a powerful tool for the promotion of an active lifestyle in the SCI population, thereby reducing the risk for secondary diseases.

A method to qualitatively assess arm use in stroke survivors in the home environment

Wearable sensor technology has enabled unobtrusive monitoring of arm movements of stroke survivors in the home environment. However, the most widely established method, based on activity counts, provides quantitative rather than qualitative information on arm without functional insights, and is sensitive to passive arm movements during ambulatory activities. We propose a method to quantify functionally relevant arm use in stroke survivors relying on a single wrist-worn inertial measurement unit. Orientation of the forearm during movements is measured in order identify gross arm movements. The method is validated in 10 subacute/chronic stroke survivors wearing inertial sensors at 5 anatomical locations for 48 h. Measurements are compared to conventional activity counts and to a test for gross manual dexterity. Duration of gross arm movements of the paretic arm correlated significantly better with the Box and Block Test ([Formula: see text]) than conventional activity counts when walking phases were included ([Formula: see text]), and similar results were found when comparing ratios of paretic and non-paretic arms for gross movements and activity counts. The proposed gross arm movement metric is robust against passive arm movements during ambulatory activities and requires only a single-sensor module placed at the paretic wrist for the assessment of functionally relevant arm use.

Monitoring Upper Limb Recovery after Cervical Spinal Cord Injury: Insights beyond Assessment Scores

BACKGROUND

Preclinical investigations in animal models demonstrate that enhanced upper limb (UL) activity during rehabilitation promotes motor recovery following spinal cord injury (SCI). Despite this, following SCI in humans, no commonly applied training protocols exist, and therefore, activity-based rehabilitative therapies (ABRT) vary in frequency, duration, and intensity. Quantification of UL recovery is limited to subjective questionnaires or scattered measures of muscle function and movement tasks.

OBJECTIVE

To objectively measure changes in UL activity during acute SCI rehabilitation and to assess the value of wearable sensors as novel measurement tools that are complimentary to standard clinical assessments tools.

METHODS

The overall amount of UL activity and kinematics of wheeling were measured longitudinally with wearable sensors in 12 thoracic and 19 cervical acute SCI patients (complete and incomplete). The measurements were performed for up to seven consecutive days, and simultaneously, SCI-specific assessments were made during rehabilitation sessions 1, 3, and 6 months after injury. Changes in UL activity and function over time were analyzed using linear mixed models.

RESULTS

During acute rehabilitation, the overall amount of UL activity and the active distance wheeled significantly increased in tetraplegic patients, but remained constant in paraplegic patients. The same tendency was shown in clinical scores with the exception of those for independence, which showed improvements at the beginning of the rehabilitation period, even in paraplegic subjects. In the later stages of acute rehabilitation, the quantity of UL activity in tetraplegic individuals matched that of their paraplegic counterparts, despite their greater motor impairments. Both subject groups showed higher UL activity during therapy time compared to the time outside of therapy time.

CONCLUSION

Tracking day-to-day UL activity is necessary to gain insights into the real impact of a patient's impairments on their UL movements during therapy and during their leisure time. In the future, this novel methodology may be used to reliably control and adjust ABRT and to evaluate the progress of UL rehabilitation in clinical trials.

Measurement of human rotation behavior for psychological and neuropsychological investigations

The investigation of rotation behavior in human beings enjoys a longstanding and enduring interest in laterality research. While in animal studies the issue of accurately measuring the number of rotations has been solved and is widely applied in practice, it is still challenging to assess the rotation behavior of humans in daily life. We propose a robust method to assess human rotation behavior based on recordings from a miniature inertial measurement unit that can be worn unobtrusively on a belt. We investigate the effect of different combinations of low-cost sensors-including accelerometers, gyroscopes, and magnetometers-on rotation measurement accuracy, propose a simple calibration procedure, and validate the method on data from a predefined path through and around buildings. Results suggest that a rotation estimation based on the fusion of accelerometer, gyroscope, and magnetometer measurements outperforms methods based solely on earth magnetic field measurements, as proposed in previous studies, by a drop in error rate of up to 32 %. We further show that magnetometer signals do not significantly contribute to measurement accuracy in short-term measurements, and could thus be omitted for improved robustness in environments with magnetic field disturbances. Results also suggest that our simple calibration procedure can compete with more complex approaches and reduce the error rate of the proposed algorithm by up to 38 %.

Accuracy-energy configurable sensor processor and IoT device for long-term activity monitoring in rare-event sensing applications

A specially designed sensor processor used as a main processor in IoT (internet-of-thing) device for the rare-event sensing applications is proposed. The IoT device including the proposed sensor processor performs the event-driven sensor data processing based on an accuracy-energy configurable event-quantization in architectural level. The received sensor signal is converted into a sequence of atomic events, which is extracted by the signal-to-atomic-event generator (AEG). Using an event signal processing unit (EPU) as an accelerator, the extracted atomic events are analyzed to build the final event. Instead of the sampled raw data transmission via internet, the proposed method delays the communication with a host system until a semantic pattern of the signal is identified as a final event. The proposed processor is implemented on a single chip, which is tightly coupled in bus connection level with a microcontroller using a 0.18 μm CMOS embedded-flash process. For experimental results, we evaluated the proposed sensor processor by using an IR- (infrared radio-) based signal reflection and sensor signal acquisition system. We successfully demonstrated that the expected power consumption is in the range of 20% to 50% compared to the result of the basement in case of allowing 10% accuracy error.

Toward real-time automated detection of turns during gait using wearable inertial measurement units

Previous studies have presented algorithms for detection of turns during gait using wearable sensors, but those algorithms were not built for real-time use. This paper therefore investigates the optimal approach for real-time detection of planned turns during gait using wearable inertial measurement units. Several different sensor positions (head, back and legs) and three different detection criteria (orientation, angular velocity and both) are compared with regard to their ability to correctly detect turn onset. Furthermore, the different sensor positions are compared with regard to their ability to predict the turn direction and amplitude. The evaluation was performed on ten healthy subjects who performed left/right turns at three amplitudes (22, 45 and 90 degrees). Results showed that turn onset can be most accurately detected with sensors on the back and using a combination of orientation and angular velocity. The same setup also gives the best prediction of turn direction and amplitude. Preliminary measurements with a single amputee were also performed and highlighted important differences such as slower turning that need to be taken into account.

Activity classification based on inertial and barometric pressure sensors at different anatomical locations

Miniature, wearable sensor modules are a promising technology to monitor activities of daily living (ADL) over extended periods of time. To assure both user compliance and meaningful results, the selection and placement site of sensors requires careful consideration. We investigated these aspects for the classification of 16 ADL in 6 healthy subjects under laboratory conditions using ReSense, our custom-made inertial measurement unit enhanced with a barometric pressure sensor used to capture activity-related altitude changes. Subjects wore a module on each wrist and ankle, and one on the trunk. Activities comprised whole body movements as well as gross and dextrous upper-limb activities. Wrist-module data outperformed the other locations for the three activity groups. Specifically, overall classification accuracy rates of almost 93% and more than 95% were achieved for the repeated holdout and user-specific validation methods, respectively, for all 16 activities. Including the altitude profile resulted in a considerable improvement of up to 20% in the classification accuracy for stair ascent and descent. The gyroscopes provided no useful information for activity classification under this scheme. The proposed sensor setting could allow for robust long-term activity monitoring with high compliance in different patient populations.

A stochastic approach to noise modeling for barometric altimeters

The question whether barometric altimeters can be applied to accurately track human motions is still debated, since their measurement performance are rather poor due to either coarse resolution or drifting behavior problems. As a step toward accurate short-time tracking of changes in height (up to few minutes), we develop a stochastic model that attempts to capture some statistical properties of the barometric altimeter noise. The barometric altimeter noise is decomposed in three components with different physical origin and properties: a deterministic time-varying mean, mainly correlated with global environment changes, and a first-order Gauss-Markov (GM) random process, mainly accounting for short-term, local environment changes, the effects of which are prominent, respectively, for long-time and short-time motion tracking; an uncorrelated random process, mainly due to wideband electronic noise, including quantization noise. Autoregressive-moving average (ARMA) system identification techniques are used to capture the correlation structure of the piecewise stationary GM component, and to estimate its standard deviation, together with the standard deviation of the uncorrelated component. M-point moving average filters used alone or in combination with whitening filters learnt from ARMA model parameters are further tested in few dynamic motion experiments and discussed for their capability of short-time tracking small-amplitude, low-frequency motions.