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PAMSys ULM™

Continuous monitoring of upper-limb movement during everyday life.

PAMSys ULM uses a wrist-worn sensor and deep-learning models to identify movement patterns associated with goal-directed upper-limb activity. Measures of movement frequency, quality, and complexity give researchers a detailed view of upper-limb use beyond the clinic.

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Everyday upper-limb activity, measured for clinical research

PAMSys ULM complements clinical assessments with measures of upper-limb movement captured during daily life. Published studies explore how these measures relate to clinical scores in neurological and neuromuscular disorders.

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A closer view of upper-limb use in daily life

Upper-limb use is central to everyday activities, from preparing meals to getting dressed. Clinic assessments provide valuable information about what a participant can do, while monitoring at home adds insight into how they move throughout the day. PAMSys ULM connects these perspectives, characterizing upper-limb movement during participants' usual routines.

The technology builds on NIH-supported research and collaboration with Spaulding Rehabilitation Hospital. Published studies include both adolescent and adult participants.

What PAMSys ULM measures

PAMSys ULM analyzes wrist acceleration to identify patterns consistent with goal-directed movements (GDMs): movements toward a target during everyday activities. It reports sensor-derived measures of movement frequency, quality, and complexity to support research on upper-limb function.

  • Goal-directed movements

    Deep-learning models classify wrist movement patterns as GDM or non-GDM, helping distinguish active upper-limb use from other motion.

  • Daily GDM counts

    Daily counts of detected GDMs summarize upper-limb activity and support comparisons across monitoring periods.

  • Movement quality

    Kinematic features describe movement patterns, including changes in acceleration and velocity, to characterize how the upper limb moves.

  • Movement complexity

    Features such as movement entropy characterize the variability and complexity of detected upper-limb movements.

More context from the same sensor

Wrist movement data add context to upper-limb measures, helping researchers explore activity patterns and develop study-specific analyses.

  • Activity intensity

    Sensor-derived estimates of activity intensity and duration, with available outputs defined by the study configuration.

  • Rest and activity patterns

    Periods of lower and higher wrist activity help researchers characterize daily rest and activity patterns.

  • Raw data access

    Access to raw accelerometer data supports custom analyses and further method development.

Designed for real-world monitoring

A lightweight sensor, extended recording, and published research support the collection of upper-limb movement data during participants' everyday routines.

  • Single wrist-worn sensor

    One lightweight wrist-worn device records movement from the monitored limb during daily activities.

  • Extended battery life

    Onboard memory and up to 6 weeks of battery life support extended recording, depending on configuration. Wireless charging simplifies recharging.

  • Evaluated in published studies

    Published studies evaluate GDM detection and examine associations between sensor-derived movement features and clinical assessments.

  • High wear adherence

    A published 7-day study in Huntington disease and controls reported 99% daily wear adherence, defined as at least 16 hours of wear per day.

  • Everyday movement patterns

    Captures upper-limb movement at home and in the community, adding context to in-clinic assessments.

  • Pairs with the PAMSys pendant

    Combine wrist-based upper-limb measures with physical activity, posture, and walking measures from the PAMSys pendant in one study.

PAMSys ULM specifications

PAMSys ULM specifications
Specification Details
Form factor Single lightweight wrist-worn sensor
Sensors 3-axis accelerometer
Analysis Deep-learning analysis of wrist acceleration to identify GDM patterns and extract movement features
Parameters 25 upper-limb activity and movement parameters in the specified configuration; available outputs depend on study setup
Battery and memory Up to 6 weeks of battery life, depending on configuration; onboard memory and wireless charging
Wear adherence monitoring Algorithm-based identification of likely non-wear periods to support wear adherence review
Data output Processed GDM features alongside full raw sensor data for custom analysis
Monitoring mode Continuous recording during wear, with subsequent data processing
Cloud integration BioDigit Cloud supports centralized study data access; data transfer and API access are defined for each study
Regulatory CE marked under EU MDR 2017/745; ISO 13485 quality management. Confirm UK MHRA registration, intended use, and study-specific data protection requirements with BioSensics.

Upper-limb research across disease cohorts

Research with PAMSys ULM spans patient and healthy control cohorts. Selected research datasets are available through BioDigit Database, subject to cohort-specific access terms. Study data are managed separately in BioDigit Cloud.

  • Healthy controls

    Movement data from healthy control participants provide a reference for comparisons within research studies.

  • Amyotrophic lateral sclerosis

    Research explores associations between GDM features and clinical measures of impairment related to amyotrophic lateral sclerosis.

  • Friedreich's ataxia

    Published research links sensor-derived movement features with established clinical measures of disease severity.

  • Huntington disease

    Published studies include prodromal and manifest cohorts and examine relationships between GDM features and clinical scores.

  • Charcot-Marie-Tooth disease

    Upper-limb monitoring is being explored in clinical research, including a gene therapy study in Charcot-Marie-Tooth disease type 4J.

  • Inclusion body myositis

    A prospective study combines wrist and pendant sensors to investigate physical activity and upper-limb movement in inclusion body myositis.

Selected publications and conference abstracts

Explore peer-reviewed studies and conference abstracts on sensor-derived upper-limb measures. Study populations, methods, and findings are described in each publication.

Part of the BioSensics ecosystem

Combine PAMSys ULM with other BioSensics solutions to collect complementary movement and assessment data. BioDigit Cloud brings study data together for centralized review.

  • Activity monitoring

    PAMSys

    Pendant sensor for monitoring body posture, postural transitions, and walking during daily life.

  • Gait monitoring

    PAMSys Gait

    Ankle-worn sensor for measuring stride-level gait patterns during everyday walking.

  • At-home monitoring

    BioDigit Home

    At-home collection of speech, video, cognitive, and patient-reported assessments on a study device.

  • Research data

    BioDigit Database

    A research resource combining digital measures and clinical data from disease-specific and healthy control cohorts.

Bring upper-limb monitoring into your study

Talk with BioSensics about the measures, monitoring schedule, and data outputs that fit your study.

Request a demo