> ## Documentation Index > Fetch the complete documentation index at: https://docs.waveyhq.dev/llms.txt > Use this file to discover all available pages before exploring further. # Ambient Elder-Care Monitoring with WiFi CSI Sensing > Ambient presence and fall-event awareness for aging in place using WiFi CSI. No cameras in bedrooms and no wearables that get left on the nightstand. Aging in place needs continuous awareness without cameras in bedrooms and without wearables that get left on the nightstand. Wavey offers ambient monitoring — presence, movement patterns, and fall-like events — from nodes already in the home. ## Why fall is not a classification problem Most early RF fall detectors train a classifier: fall vs walk vs sit. That assumes falls produce reproducible CSI signatures you can label and match. In practice, falls are accidents — impact varies with surface, body angle, and what they hit. Labeling enough real elderly falls to train a classifier is ethically and practically hard; most training data is simulated. SiFall (SenSys 2022) reframes the problem with an anomaly-detection approach: An autoencoder (FallNet) models the distribution of repeatable daily activities (walking, sitting, standing) for this person in this room. CSI amplitude (not phase) → conjugate multiplication denoising → channel dynamics via cosine similarity across subcarrier vectors → acceleration threshold (Θ = 2.5 m/s²) to segment candidate events → STFT → FallNet input. A fall produces a CSI segment with high reconstruction error because it does not match any learned normal pattern. Self-supervised incremental learning updates the normal-activity model as routines evolve, without requiring fall labels. Reported 98.3% in real-world tests, 94.1% over 3-day continuous monitoring with one false alarm. This is fundamentally different from spike-plus-immobility heuristics. It handles variable fall kinematics because anything outside the routine distribution triggers — not because it matched a fall template. Limitations from the published system: largely single-room operation, simulated fall data, and unvalidated on subjects over 60. False positives from unusual but non-fall activities (dropping onto a couch) remain a tuning problem. ## Inactivity monitoring Less dramatic but equally useful: **prolonged absence of expected motion**. If someone who normally moves through the kitchen every morning has not triggered motion by noon, that is a signal worth surfacing — built on [presence detection](/use-cases/presence-detection) and motion baselines, not fall classification. Routine patterns emerge from temporal aggregation over days: typical wake time, movement frequency, room transitions. Deviations from routine are the alert, not a single event classifier. ## What Wavey is not Wavey is an **awareness layer**, not a medical device: * No certified fall detection with regulatory clearance. * No clinical-grade respiration rate or heart rate. * No identity — the system does not know who fell, only that an anomaly occurred in a zone. For the full feasibility picture, see fall detection at rung 6 on the [detection ladder](/concepts/detection-ladder). ## Deployment considerations * **Node placement** — links should cross high-traffic areas (hallway, bedroom doorway) where falls are most likely to perturb CSI. * **Baseline stability** — furniture changes and new occupants require re-baselining. * **Multi-person homes** — superposition makes per-person fall attribution unreliable without spatial diversity. ## Further Reading Confirming someone is still in the room Activity patterns over time Fall detection feasibility and limits Set up your first Wavey deployment