Common devices for liikemittauksia include pedometers, accelerometers, inertial measurement units, heart rate monitors, and wearable sensors that combine multiple modalities. Accelerometers measure acceleration across one or more axes, producing counts that can be translated into steps, energy expenditure, or activity categories. Pedometers primarily count steps, while heart rate monitors infer intensity through physiological responses. Wearable sensors may also capture spatiotemporal gait metrics, posture stability, and limb kinematics.
Software algorithms convert raw sensor data into interpretable metrics. Activity classification algorithms discriminate between walking, running, cycling, and sedentary behaviors. Energy expenditure estimates often rely on calibrated regression models that incorporate age, gender, weight, and activity intensity. Wearable technology has advanced to incorporate Bluetooth connectivity, cloud-based data storage, and real-time feedback, supporting personalized coaching and continuous monitoring.
Clinical applications of liikemittauksia include monitoring patients with musculoskeletal injuries, cardiovascular conditions, or neurodegenerative disorders. Rehabilitation protocols often employ objective movement data to gauge adherence and functional gains. In occupational settings, movement measurements help assess ergonomic risk and inform workplace design. Public health initiatives use population-level activity data to track physical activity trends, evaluate policy interventions, and inform community planning.
Standards for measurement validity and reliability have been established by organizations such as the American College of Sports Medicine and the Nordic Hurren Group. These guidelines address sensor placement, data sampling rates, and calibration procedures to ensure consistency across studies. The increasing availability of low-cost wearables has broadened research participation, but researchers must still address data privacy, consent, and ethical considerations in handling personal movement data.