Muscle fatigue during assisted violin performance.

Muscle fatigue is a primary risk factor in developing musculoskeletal disorders, which affect up to 93% musicians, especially violinists. Devices providing dynamic assistive support (DAS) to the violin-holding arm can lessen fatigue. The objective was to assess DAS effects on electromyography median frequency and joint kinematics during a fatiguing violin-playing task. Fifteen university-level and professional violinists were equipped with electromyography sensors and reflective markers to record upper-body muscle activity and kinematics. They played G scales with and without DAS until exhaustion. Paired t-tests assessed DAS effects on delta (final-initial) electromyography median frequencies and joint kinematics. DAS prevented the median frequency decrease of left supraspinatus, superior trapezius, and right medial deltoid, and increases in trunk rotation, left-wrist abduction, and right arm-elevation plane. DAS effects on kinematics were marginal due to retention of musical performance despite fatigue. However, DAS reduced fatigue of several muscles, which is promising for injury prevention.Practitioner summary: Violinists are greatly affected by musculoskeletal disorders. Effects of a mobility assistive device on muscle fatigue during violin playing was investigated. The assistive technology slowed down the development of fatigue for three neck/shoulder muscles, making assisted musical performance a promising avenue to prevent violinists' injuries.

Neuromotor variability partially explains different endurance capacities of expert pianists.

During fatiguing piano tasks, muscle fatigue develops differently between expert pianists. Differences in neuromotor strategies employed could explain a slower rate of fatigue development. The objective was to compare muscle activation and kinematic variabilities between ShortDuration (i.e., pianists with less endurance) and LongDuration groups. Results from 49 pianists showed that EMG activation variability of most shoulder and upper limbs muscles was greater for the ShortDuration group with time during two piano fatiguing tasks, namely Digital and Chord tasks. Segment acceleration variability, assessed using inertial measurement units, was also greater with time for the ShortDuration group at the right arm during the Digital task, and at the thorax and head during the Chord task. Finally, thorax lateroflexion variability increased with time for the LongDuration group (but not the ShortDuration group) during the Digital task. During the Chord task, wrist flexion variability was higher for the LongDuration group compared to the ShortDuration group. These results showed a direct effect of time on the pianists' acceleration variability and EMG activation variability. In contrast, a protective effect of fatigue development could be attributed to kinematic variability. Results also suggest a higher risk of injury among pianists in the ShortDuration group.

The influence of proximal motor strategies on pianists' upper-limb movement variability.

Repetitive movements are considered a risk factor for developing practice-related musculoskeletal disorders. Intra-participant kinematic variability might help musicians reduce the risk of injury during repetitive tasks. No research has studied the effects of proximal motion (i.e., trunk and shoulder movement) on upper-limb movement variability in pianists. The first objective was to determine the effect of proximal movement strategies and performance tempo on both intra-participant joint angle variability of upper-limb joints and endpoint variability. The second objective was to compare joint angle variability between pianist's upper-limb joints. As secondary objectives, we assessed the relationship between intra-participant joint angle variability and task range of motion (ROM) and documented inter-participant joint angle variability. The upper body kinematics of 9 expert pianists were recorded using an optoelectronic system. Participants continuously performed two right-hand chords (lateral leap motions) while changing movements based on trunk motion (with and without) and shoulder motion (counter-clockwise, back-and-forth, and clockwise) at two tempi (slow and fast). Trunk and shoulder movement strategies collectively influenced variability at the shoulder, elbow and, to a lesser extent, the wrist. Slow tempi led to greater variability at wrist and elbow flexion/extension compared to fast tempi. Endpoint variability was influenced only along the anteroposterior axis. When the trunk was static, the shoulder had the lowest joint angle variability. When trunk motion was used, elbow and shoulder variability increased, and became comparable to wrist variability. ROM was correlated with intra-participant joint angle variability, suggesting that increased task ROM might result in increased movement variability during practice. Inter-participant variability was approximately six times greater than intra-participant variability. Pianists should consider incorporating trunk motion and a variety of shoulder movements as performance strategies while performing leap motions at the piano, as they might reduce exposure to risks of injury.

Power Spectrum of Acceleration and Angular Velocity Signals as Indicators of Muscle Fatigue during Upper Limb Low-Load Repetitive Tasks.

Muscle fatigue is a risk factor for developing musculoskeletal disorders during low-load repetitive tasks. The objective of this study was to assess the effect of muscle fatigue on power spectrum changes of upper limb and trunk acceleration and angular velocity during a repetitive pointing task (RPT) and a work task. Twenty-four participants equipped with 11 inertial measurement units, that include acceleration and gyroscope sensors, performed a tea bag filling work task before and immediately after a fatiguing RPT. During the RPT, the power spectrum of acceleration and angular velocity increased in the movement and in 6-12 Hz frequency bands for sensors positioned on the head, sternum, and pelvis. Alternatively, for the sensor positioned on the hand, the power spectrum of acceleration and angular velocity decreased in the movement frequency band. During the work task, following the performance of the fatiguing RPT, the power spectrum of acceleration and angular velocity increased in the movement frequency band for sensors positioned on the head, sternum, pelvis, and arm. Interestingly, for both the RPT and work task, Cohens' d effect sizes were systematically larger for results extracted from angular velocity than acceleration. Although fatigue-related changes were task-specific between the RPT and the work task, fatigue systematically increased the power spectrum in the movement frequency band for the head, sternum, pelvis, which highlights the relevance of this indicator for assessing fatigue. Angular velocity may be more efficient to assess fatigue than acceleration. The use of low cost, wearable, and uncalibrated sensors, such as acceleration and gyroscope, in industrial settings is promising to assess muscle fatigue in workers assigned to upper limb repetitive tasks.

Effect of Expertise on Shoulder and Upper Limb Kinematics, Electromyography, and Estimated Muscle Forces During a Lifting Task.

OBJECTIVE: To highlight the working strategies used by expert manual handlers compared with novice manual handlers, based on recordings of shoulder and upper limb kinematics, electromyography (EMG), and estimated muscle forces during a lifting task. BACKGROUND: Novice workers involved in assembly, manual handling, and personal assistance tasks are at a higher risk of upper limb musculoskeletal disorders (MSDs). However, few studies have investigated the effect of expertise on upper limb exposure during workplace tasks. METHOD: Sixteen experts in manual handling and sixteen novices were equipped with 10 electromyographic electrodes to record shoulder muscle activity during a manual handling task consisting of lifting a box (8 or 12 kg), instrumented with three six-axis force sensors, from hip to eye level. Three-dimensional trunk and upper limb kinematics, hand-to-box contact forces, and EMG were recorded. Then, joint contributions, activation levels, and muscle forces were calculated and compared between groups. RESULTS: Sternoclavicular-acromioclavicular joint contributions were higher in experts at the beginning of the movement, and in novices at the end, whereas the opposite was observed for the glenohumeral joint. EMG activation levels were 37% higher for novices but predicted muscle forces were higher in experts. CONCLUSION: This study highlights significant differences between experts and novices in shoulder kinematics, EMG, and muscle forces; hence, providing effective work guidelines to ensure the development of a safe handling strategy is important. APPLICATION: Shoulder kinematics, EMG, and muscle forces could be used as ergonomic tools to identify inappropriate techniques that could increase the prevalence of shoulder injuries.

Shoulder electromyography-based indicators to assess manifestation of muscle fatigue during laboratory-simulated manual handling task.

Muscle fatigue is a risk factor for developing shoulder musculoskeletal disorders. The aim of this study was to identify shoulder electromyographic indicators that are most indicative of muscle fatigue during a laboratory simulated manual handling task. Thirty-two participants were equipped with electromyographic electrodes on 10 shoulder muscles and moved boxes for 45-minutes. The modified rate of perceived exertion (mRPE) was assessed every 5-minutes and multivariate linear regressions were performed between myoelectric manifestation of fatigue (MMF) and the mRPE scores. During a manual handling task representative of industry working conditions, spectral entropy, median frequency, and mobility were the electromyographic indicators that explained the largest percentage of the mRPE. Overall, the deltoids, biceps and upper trapezius were the muscles that most often showed significant changes over time in their electromyographic indicators. The combination of these three indicators may improve the accuracy for the assessment of MMF during manual handling. Practitioner Summary: To date, muscle fatigue has primarily been assessed during tasks done to exhaustion, which are not representative of typical working conditions. During a manual handling task representative of industry working conditions, EMG-derived spectral entropy, and median frequency, both extracted from time-frequency analysis, and mobility extracted from time domain, were the best indicators of the manifestation of muscle fatigue.

Exhausting repetitive piano tasks lead to local forearm manifestation of muscle fatigue and negatively affect musical parameters.

Muscle fatigue is considered as a risk factor for developing playing-related muscular disorders among professional pianists and could affect musical performance. This study investigated in 50 pianists the effect of fatiguing repetitive piano sequences on the development of forearm muscle fatigue and on piano performance parameters. Results showed signs of myoelectric manifestation of fatigue in the 42-electromyographic bipolar electrodes positioned on the forearm to record finger and wrist flexor and extensor muscles, through a significant non-constant decrease of instantaneous median frequency during two repetitive Digital (right-hand 16-tones sequence) and Chord (right-hand chords sequence) excerpts, with extensor muscles showing greater signs of fatigue than flexor muscles. In addition, muscle fatigue negatively affected key velocity, a central feature of piano sound intensity, in both Digital and Chord excerpts, and note-events, a fundamental aspect of musicians' performance parameter, in the Chord excerpt only. This result highlights that muscle fatigue may alter differently pianists' musical performance according to the characteristics of the piece played.

Parkinsonian Symptoms, Not Dyskinesia, Negatively Affect Active Life Participation of Dyskinetic Patients with Parkinson's Disease.

Background: The impact of slight-to-moderate levodopa-induced dyskinesia (LID) on the level of participation in active life in patients with Parkinson's disease (PD) has never been objectively determined. Methods: Levels of LID, tremor and bradykinesia were measured during best-ON state in 121 patients diagnosed with PD and having peak-dose LID using inertial sensors positioned on each body limb. Rigidity and postural instability were assessed using clinical evaluations. Cognition and depression were assessed using the MMSE and the GDS-15. Participation in active life was assessed in patients and in 69 healthy controls using the Activity Card Sort (ACS), which measures levels of activity engagement and activities affected by the symptomatology. Outcome measures were compared between patients and controls using ANCOVA, controlling for age or Wilcoxon-Mann-Whitney tests. Spearman correlations and multivariate analyses were then performed between symptomatology and ACS scores. Results: Patients had significantly lower activity engagement than controls and had significantly affected activities. LID was neither associated with activity engagement nor affected activities. Higher levels of tremor, postural instability, cognitive decline and depression were associated with lower activity engagement and higher affected activities. Multivariate analyses revealed that only tremor, postural instability and depression accounted significantly in the variances of these variables. Discussion: Slight-to-moderate LID had little impact compared to other symptoms on the level of participation in active life, suggesting that other symptoms should remain the treatment priority to maintain the level of participation of patients in an active lifestyle.

Sex differences in upper limb musculoskeletal biomechanics during a lifting task.

Women experience higher prevalence of work-related upper limb musculoskeletal disorders compared to men. Previous studies have investigated the biological, kinematic and electromyographic sex-related differences during a lifting task but the actual differences in musculoskeletal loads remain unknown. We investigated the sex differences in three musculoskeletal indicators: the sum of muscle activations, the sum of muscle forces and the relative time spent beyond a shear-compression dislocation ratio. A musculoskeletal model was scaled on 20 women and 20 men lifting a 6 or 12kg box from hip to eye level. Women generated more muscle forces and activations than men, regardless of the lifted mass. Those differences occurred when the box was above shoulder level. In addition, women might spend more time beyond a shear-compression dislocation ratio. Our work suggests higher musculoskeletal loads among women compared to men during a lifting task, which could be the result of poor technique and strength difference.

Can We Predict the Motor Performance of Patients With Parkinson's Disease Based on Their Symptomatology?

Introduction: Parkinson's disease hinders the ability of a person to perform daily activities. However, the varying impact of specific symptoms and their interactions on a person's motor repertoire is not understood. The current study investigates the possibility to predict global motor disabilities based on the patient symptomatology and medication. Methods: A cohort of 115 patients diagnosed with Parkinson's disease (mean age = 67.0 ± 8.7 years old) participated in the study. Participants performed different tasks, including the Timed-Up & Go, eating soup and the Purdue Pegboard test. Performance on these tasks was judged using timing, number of errors committed, and count achieved. K-means method was used to cluster the overall performance and create different motor performance groups. Symptomatology was objectively assessed for each participant from a combination of wearable inertial sensors (bradykinesia, tremor, dyskinesia) and clinical assessment (rigidity, postural instability). A multinomial regression model was derived to predict the performance cluster membership based on the patients' symptomatology, socio-demographics information and medication. Results: Clustering exposed four distinct performance groups: normal behavior, slightly affected in fine motor tasks, affected only in TUG, and affected in all areas. The statistical model revealed that low to moderate level of dyskinesia increased the likelihood of being in the normal group. A rise in postural instability and rest tremor increase the chance to be affected in TUG. Finally, LEDD did not help distinguishing between groups, but the presence of Amantadine as part of the medication regimen appears to decrease the likelihood of being part of the groups affected in TUG. Conclusion: The approach allowed to demonstrate the potential of using clinical symptoms to predict the impact of Parkinson's disease on a person's mobility performance.

Remnants of Cardinal Symptoms of Parkinson's Disease, Not Dyskinesia, Are Problematic for Dyskinetic Patients Performing Activities of Daily Living.

Introduction: The impact of levodopa-induced dyskinesia (LID) on the daily lives of patients with Parkinson's disease (PD) remains to be determined. Furthermore, evidence suggests that cardinal motor symptoms of PD may coexist with LID, but their impact on activities of daily living (ADL) relative to LID is not known. This cross-sectional study aimed at determining the effect of LID and cardinal motor symptoms of PD on ADL in patients who were experiencing peak-dose choreic-type LID. Method: One hundred and twenty-one patients diagnosed with PD known to experience choreic-type LID were recruited for the study. Patients were asked to perform a set of ADL. Levels of LID, tremor, bradykinesia, and freezing of gait (FoG) were measured using 17 inertial sensors design to capture full body movements, while rigidity, and postural instability were assessed using clinical evaluations. Cognition was also assessed using the mini-mental state examination. Success criteria were set for each ADL using the time needed to perform the task and errors measured in 69 age-gender-matched healthy controls. Binary logistic regressions were used to identify symptoms influencing success or failure for each activity. Receiver operating characteristic curves were computed on each significant symptom, and Youden indexes were calculated to determine the critical level of symptomatology at which the performance significantly changed. Results: Results show that 97.7% of patients who presented with LID during the experiment also presented with at least one cardinal motor symptom. On average, patients took more time and did more errors during ADL. Multivariate analyses revealed that for the great majority of ADL, LID were not associated with worsening of performance; however, postural instability, tremor, rigidity, and cognitive decline significantly decreased the odds of success. Conclusions: Residual symptoms of PD, such as tremor, rigidity, and postural instability still present at peak-dose were more problematic than LID in the performance of ADL for patients experiencing slight-to-moderate LID. We also found that cognitive decline was associated with decreased performance in certain tasks. Therefore, a strategy using lower doses of medication to manage LID may be counterproductive since it would not address most of these symptoms already present in patients.

Parkinson's disease patients experiencing peak-dose dyskinesia redistribute involuntary movements throughout their body to improve motor control.

INTRODUCTION: In Parkinson's disease (PD), dyskinesia is considered a major side effect of dopamine replacement therapy. Nevertheless, many patients with dyskinesia function adequately. OBJECTIVE: To study objectively dyskinesia phenomenology in order to understand why or how patients with dyskinesia are still able to perform motor tasks. METHODS: Patients with and without dyskinesia, as well as healthy older adults, performed a geostationary task during which they attempted to stabilize a glass of water at eye level. Dyskinesia amplitude displayed by each body segment was extracted from accelerometers, and its distribution among the segments, analyzed. RESULTS: Patients experiencing dyskinesia initially distributed most of their dyskinesia away from the segments directly involved in the task. With time, this distribution shifts back towards the hand. CONCLUSION: Our results suggest that patients developed a strategy of involuntary movement's redistribution to attenuate their functional impact on voluntary movements. However, this strategy can only be maintained for a certain period before "re-emerging" dyskinesia occurs.

Cardinal Motor Features of Parkinson's Disease Coexist with Peak-Dose Choreic-Type Drug-Induced Dyskinesia.

BACKGROUND: Clinical and anecdotal observations propose that patients with Parkinson's disease (PD) may show drug-induced dyskinesia (DID) concomitantly with cardinal motor features. However, the extent of the concomitant presence of DID and cardinal features remains to be determined. OBJECTIVES: This cross-sectional study measured peak-dose choreic-type DID in a quantitative manner in patients diagnosed with PD, and determined whether symptoms such as tremor, bradykinesia, rigidity, postural instability or freezing of gait (FoG) were still detectable in these patients. METHODS: 89 patients diagnosed with PD were recruited and assessed using a combination of quantitative measures using inertial measurement units to capture DID, tremor, bradykinesia, and FoG. Clinical evaluations were also used to assess rigidity and postural instability. Motor symptoms of PD were assessed 3 times during the testing period, and a series of activities of daily living were repeated twice, in between clinical tests, during which the level of DID was quantified. Peak-dose was identified as the period during which patients had the highest levels of DID. Levels of tremor, rigidity, bradykinesia, postural instability, and FoG were used to determine the percentage of patients showing these motor symptoms simultaneously with DID. RESULTS: 72.4% of patients tested presented with measurable DID during the experiment. Rest, postural and kinetic tremor (12.7% , 38.1% , and 15.9% respectively), bradykinesia (28.6% ), rigidity (55.6% ), postural instability (71.4% ) and FoG (9.5% ) were detected simultaneously with DID. CONCLUSIONS: PD symptomatology remains present in patients showing peak-dose choreic-type DID, illustrating the challenge facing physicians when trying to avoid dyskinesia while attempting to alleviate motor symptoms.

Using Inertial Sensors to Automatically Detect and Segment Activities of Daily Living in People With Parkinson's Disease.

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.

Do Bradykinesia and Tremor Interfere in Voluntary Movement of Essential Tremor Patients? Preliminary Findings.

BACKGROUND: The aim of this study was to determine whether tremor and bradykinesia impacted a dexterous activity performed by patients with essential tremor (ET). METHODS: Core bradykinesia was assessed in 27 controls and 15 patients with ET using a rapid alternating movement (RAM) task. Then, participants performed a "counting money" counting tasks while equipped with inertial measurement units to detect and quantify tremor during movement. The time required to perform subsections of the tasks and the rate of failure (errors) were compared between groups using Mann-Whitney U tests and a chi-square test, respectively. RESULTS: Patients with ET presented with significant bradykinesia during the RAM task and had more tremor during the counting money task. However, the time required to perform the task and rate of failure were similar between groups. DISCUSSION: Results show that even though bradykinesia was detected during fast movements, and that tremor was present during a task requiring dexterity, both symptoms did not interfere with the performance of patients with ET. This pilot study suggests that there may be a threshold at which tremor will become problematic. Determining this threshold for a wide range of daily activities may help determine when it is appropriate to initiate treatment for patients with ET.

Auto detection and segmentation of daily living activities during a Timed Up and Go task in people with Parkinson's disease using multiple inertial sensors.

BACKGROUND: 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). METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Wavelet-based algorithm for auto-detection of daily living activities of older adults captured by multiple inertial measurement units (IMUs).

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.

The Use of Empirical Mode Decomposition-Based Algorithm and Inertial Measurement Units to Auto-Detect Daily Living Activities of Healthy Adults.

he use of inertial measurement units (IMUs) in motion analysis for clinical purpose is relatively recent. However, the use of such system in free environment remains sparse. This is in part due the lack of robust algorithms to handle large volumes of data for performance evaluation and patient diagnosis. The present work examines the ability of using Empirical Mode Decomposition and discrete-time detection of events to automatically detect and segment tasks associated with activities of daily living (ADL) using IMUs. Seven healthy older adults (73± 4 years old) performed ADL tasks in a simulated apartment during trials of different durations (3, 4, and 5-min). They wore a suit (Synertial-IGS180) comprised of 17-IMUs positioned strategically on body segments to capture full body motion. After a systematic process examining time series of each sensor, it was determined that 6-IMUs were sufficient to detect the 9 tasks at hand (such as walking, sit to stand, stand to sit, reaching to the ground to pick or to put down objects on the floor, step an obstacle and turning). The proposed method automatically identified the proper set of template waveforms associated to ADL tasks based on kinematic data acquired from the selected IMUs. The ground truth on timing of tasks was established by visual segmentation of recordings using the system's software. Despite the variation in the occurrences of the performed tasks (freely moving), the proposed algorithm exhibited high global accuracy under unscripted conditions of motion, for both Se. and Sp. of 97% (Nevents=1999), using a few features and without learning process. This work will eventually allow for the assessment of mobility performance within the segmented signals; specifically how well the person is moving in his/her environment.