Wearable sensors such as inertial measurement units (IMUs) have been widely used to measure the quantity of physical activities during daily living in healthy and people with movement disorders through activity classification. These sensors have the potential to provide valuable information to evaluate the quality of the movement during the activities of daily living (ADL), such as walking, sitting down, and standing up, which could help clinicians to monitor rehabilitation and pharmaceutical interventions. However, high accuracy in the detection and segmentation of these activities is necessary for proper evaluation of the quality of the performance within a given segment. This paper presents algorithms to process IMU data, to detect and segment unstructured ADL in people with Parkinson's disease (PD) in simulated free-living environment. The proposed method enabled the detection of 1610 events of ADL performed by nine community dwelling older adults with PD under simulated free-living environment with 90% accuracy (sensitivity = 90.8%, specificity = 97.8%) while segmenting these activities within 350 ms of the “gold-standard” manual segmentation. These results demonstrate the robustness of the proposed method to eventually be used to automatically detect and segment ADL in free-living environment in people with PD. This could potentially lead to a more expeditious evaluation of the quality of the movement and administration of proper corrective care for patients who are under physical rehabilitation and pharmaceutical intervention for movement disorders.

Wearable sensors have the potential to provide clinicians with access to motor performance of people with movement disorder as they undergo intervention. However, sensor data often have to be manually classified and segmented before they can be processed into clinical metrics. This process can be time consuming. We recently proposed detection and segmentation algorithms based on peak detection using Inertial Measurement Units (IMUs) to automatically identify and isolate common activities during daily living such as standing up, walking, turning, and sitting down. These algorithms were developed using a homogenous population of healthy older adults. The aim of this study was to investigate the transferability of these algorithms in people with Parkinson’s disease (PD). A modified Timed Up And Go task was used since it is comprised of these activities, all performed in a continuous fashion. Twelve older adults diagnosed with early PD (Hoehn & Yahr ≤ 2) were recruited for the study and performed three trials of a 10 and 5-m TUG during OFF state. They were outfitted with 17 IMUs covering each body segment. Raw data from IMUs were detrended, normalized and filtered to reveal kinematics peaks that corresponded to different activities. Segmentation was accomplished by identifying the first minimum or maximum to the right and the left of these peaks. Segmentation times were compared to results from two examiners who visually segmented the activities. Specificity and sensitivity were used to evaluate the accuracy of the detection algorithms. Using the same IMUs and algorithms developed in the previous study, we were able to detect these activities with 97.6% sensitivity and 92.7% specificity (n = 432) in PD population. However, with modifications to the IMUs selection, we were able to detect these activities with 100% accuracy. Similarly, applying the same segmentation to PD population, we were able to isolate these activities within ~500 ms of the visual segmentation. Re-optimizing the filtering frequencies, we were able to reduce this difference to ~400 ms. This study demonstrates the agility and transferability of using a system of IMUs to accurately detect and segment activities in daily living in people with movement disorders.

Joints kinematics assessment based on inertial measurement systems, which include attitude and heading reference system (AHRS), are quickly gaining in popularity for research and clinical applications. The variety of the tasks and contexts they are used in require a deep understanding of the AHRS accuracy for optimal data interpretation. However, published accuracy studies on AHRS are mostly limited to a single task measured on a limited number of segments and participants. This study assessed AHRS sensors kinematics accuracy at multiple segments and joints through a variety of tasks not only to characterize the system’s accuracy in these specific conditions, but also to extrapolate the accuracy results to a broader range of conditions using the characteristics of the movements (i.e. velocity and type of motion). Twenty asymptomatic adults ( $$\overline{age}$$ = 49.9) performed multiple 5 m timed up and go. Participants’ head, upper trunk, pelvis, thigh, shank and foot were simultaneously tracked using AHRS and an optical motion capture system (gold standard). Each trial was segmented into basic tasks (sit-to-stand, walk, turn). At segment level, results revealed a mean root-mean-squared-difference $$\overline{(RMSD)}$$ varying between 1.1° and 5.5° according to the segment tracked and the task performed, with a good to excellent agreement between the systems. Relative sensor kinematics accuracy (i.e. joint) varied between 1.6° and 13.6° over the same tasks. On a global scheme, analysis of the effect of velocity on sensor kinematics accuracy showed that AHRS are better adapted to motions performed between 50°/s and 75°/s (roughly thigh and shank while walking). Results confirmed that pairing of modules to obtain joint kinematics affects the accuracy compared to segment kinematics. Overall, AHRS are a suitable solution for clinical evaluation of biomechanics under the multi-segment tasks performed although the variation in accuracy should be taken into consideration when judging the clinical meaningfulness of the observed changes.

Recently, much attention has been given to the use of inertial sensors for remote monitoring of individuals with limited mobility. However, the focus has been mostly on the detection of symptoms, not specific activities. The objective of the present study was to develop an automated recognition and segmentation algorithm based on inertial sensor data to identify common gross motor patterns during activity of daily living. A modified Time-Up-And-Go (TUG) task was used since it is comprised of four common daily living activities; Standing, Walking, Turning, and Sitting, all performed in a continuous fashion resulting in six different segments during the task. Sixteen healthy older adults performed two trials of a 5 and 10 meter TUG task. They were outfitted with 17 inertial motion sensors covering each body segment. Data from the 10 meter TUG were used to identify pertinent sensors on the trunk, head, hip, knee, and thigh that provided suitable data for detecting and segmenting activities associated with the TUG. Raw data from sensors were detrended to remove sensor drift, normalized, and band pass filtered with optimal frequencies to reveal kinematic peaks that corresponded to different activities. Segmentation was accomplished by identifying the time stamps of the first minimum or maximum to the right and the left of these peaks. Segmentation time stamps were compared to results from two examiners visually segmenting the activities of the TUG. We were able to detect these activities in a TUG with 100% sensitivity and specificity (n = 192) during the 10 meter TUG. The rate of success was subsequently confirmed in the 5 meter TUG (n = 192) without altering the parameters of the algorithm. When applying the segmentation algorithms to the 10 meter TUG, we were able to parse 100% of the transition points (n = 224) between different segments that were as reliable and less variable than visual segmentation performed by two independent examiners. The present study lays the foundation for the development of a comprehensive algorithm to detect and segment naturalistic activities using inertial sensors, in hope of evaluating automatically motor performance within the detected tasks.

A recent trend in human motion capture is the use of inertial measurement units (IMUs) for monitoring and performance evaluation of mobility in the natural living environment. Although the use of such systems have grown significantly, the development of methods and algorithms to process IMU data for clinical purposes is still limited. The aim of this work is to develop algorithms based on wavelet transform and discrete-time detection of events for the automatic segmentation of tasks related activities of daily living (ADL) from body worn IMUs. Seven healthy older adults (73 ± 4 years old) performed 10 ADL tasks in a simulated apartment during trials of different durations (3, 4, and 5 min). They wore a suit (Synertial UK Ltd IGS-180) comprised of 17 IMUs positioned strategically on body segments to capture full body motion. The proposed method automatically detected the number of template waveforms (representing each movement separately) using discrete wavelet transform (DWT) and discrete-time detection of events based on angular velocity, linear acceleration and 3D orientation data of pertinent IMUs. The sensitivity (Se.) and specificity (Sp.) of detection for the proposed method was established using time stamps of10tasks obtained from visual segmentation of each trial using the video records and the avatar provided by the system's software. At first, we identified six pertinent sensors that were strongly associated to different activities (at most two sensors/task) that allowed detection of tasks with high accuracy. The proposed algorithm exhibited significant global accuracy (N events = 1999, Se. = 97.5%, Sp. = 94%), despite the variation in the occurrences of the performed tasks (free living). The Se. varied from 94% to 100% for all the detected ADL tasks and Sp. ranged from 90% to 100% with the worst Sp. = 85 and 87% for Release_mid (reaching for object held just beyond reach at chest height) and Turning_Left tasks, respectively. This study demonstrated that DWT in conjunction with a nonlinear transform and auto-adaptive thresholding process for decision rules are highly efficient in detecting and segmenting tasks performed during free-living activities. This study also helped to determine the optimal number of sensors, and their location to detect such activities. This work lays the foundation for the automatic assessment of mobility performance within the segmented signals, as well as potentially helps differentiate populations based on their mobility patterns and symptomatology.