Geriatric 4Ms Framework

• Geriatric 4Ms is a framework that defines person-centered care through four key domains: What Matters Most, Mentation, Mobility, and Medication.
• The framework integrates iterative hardware design with advanced sensor analytics and TCN-based inference to deliver real-time, privacy-preserving remote monitoring.
• It emphasizes actionable insights by aligning user interfaces and LLM-assisted configurations to support aging-in-place with robust, low-burden technology.

The Geriatric 4Ms Framework provides a structured approach to systematize care for older adults, particularly in the context of technology-enabled remote monitoring. It comprises four core domains: "What Matters Most," "Mentation," "Mobility," and "^^^^3^^^^" each guiding hardware, modeling, and user-interface design to align technology deployment with the specific needs, preferences, and vulnerabilities of the elderly population. The framework aims to ensure that new solutions, such as remote home health monitoring systems, are both technically robust and person-centered, maintaining privacy, minimizing user burden, and supporting real-time actionable insights in support of aging-in-place (Lee et al., 24 Jan 2026).

1. Conceptual Definition: The 4Ms Domains

The "4Ms" refer to distinct but interrelated priorities in geriatric care:

• What Matters Most: Captures the personal goals, privacy concerns, and daily rhythms of older adults. For system design, this necessitates discreet hardware (e.g., unobtrusive wall-plug enclosures) and the exclusion of intrusive modalities such as cameras or microphones, using only vibration sensors. Customizable event alert thresholds are implemented to ensure that caregivers are only notified about relevant events (e.g., missed pill-box openings).
• Mentation: Encompasses support for cognitive function and emotional well-being. The system responds through tamper-resistant packaging, concealed wiring, and minimal required user interaction—eliminating the need for software installations or post-setup configuration.
• Mobility: Focuses on monitoring physical activity indicative of function and fall risk, including gait, sit-to-stand transitions, and bathroom visits. High-frequency geophone sensors (sampling at 7 kHz) are distributed throughout the environment. On-device, real-time analysis via Temporal Convolutional Network (TCN) inference detects activities such as walking or showering.
• Medication: Concerns the tracking of self-administered medication events, using vibration-based detection of pill-box or bottle handling. Missed scheduled events are logged and communicated to caregivers in near-real time.

Systematic integration of the 4Ms at all levels of system architecture is fundamental to achieve a person-centered, robust, and privacy-preserving remote monitoring solution (Lee et al., 24 Jan 2026).

2. Hardware Design and Deployment Iterations

Deployment of the 4Ms framework in remote monitoring necessitated iterative hardware and network refinements, with distinct responses to privacy, plug-and-play requirements, and user acceptance.

Deployment	Focus	Hardware	Network
1	System validation	ESP32S3 + ADS131M02 + geophone, bare PCB, 7 kHz	UDP, congestion issues
2	User feasibility	Same sensors, white enclosure, geophone clipped	UDP + packet-level parity repair
3(A/B)	System + User	Discreet enclosure, concealed geophones, 6.8kHz	UDP + parity, Wireguard tunnel

Later iterations introduced an aesthetically integrated enclosure, improved wiring concealment, and local as well as encrypted cloud connectivity (via Wireguard) to address privacy and reliability. LLM-guided sensor placement improved untampered rates and SNR balance, with deployment 3(A) achieving both 100% untampered installations and high signal-to-noise ratios (Lee et al., 24 Jan 2026).

The final block-diagram includes:

• Geophone with 10 Hz–1 kHz passband → ADS131M02 ADC → ESP32S3 MCU
• UDP with parity → Wi-Fi → Edge Hub (Raspberry Pi or similar): TCN inference, local storage (70GB/day), health monitoring, cloud telemetry via encrypted tunnel

3. Modeling, Signal Processing, and Performance Metrics

Data Collection and Annotation

Ground truth is established through scripted, in-situ activities mapped to vibration traces and video-synchronized annotation (1–2 hours per home, at 1 s or 30 min resolution depending on deployment phase).

Activity Recognition Using TCN

The TCN model adopts a multi-task learning paradigm:

• Input: window $x\in\mathbb{R}^{L\times1}$
• Encoder: $z = h_\theta(x)$
• Reconstruction Head: $x' = g_\phi(z)$
• Classification Head: $\hat{y} = \text{softmax}(W\cdot z + b)$

Loss function:

$$L(\theta,\phi,W,b) = \lambda\cdot\|x-x'\|_2^2 + (1-\lambda)\cdot\text{CE}(y,\hat{y})$$

Models are pretrained on audio (acoustic dataset) and transferred to vibration before fine-tuning with in-home data.

Medication Adherence

Medication events are defined as sharp vibration transients. Absence of events within expected dosing windows triggers notification logic. Event-markers populate a time-stamped medication log.

Evaluation Metrics

• Sampling-rate consistency ($\sigma_f$): improved to $316$ Hz deviation in deployment 3, from $734$ Hz and $799$ Hz in prior phases.
• Signal-to-noise ratio (SNR): Highest in deployment 2 (0% untampered), balanced in 3(A) (100% untampered).
• t-SNE separation (perplexity=$30$, KL=0.7438) confirms class discrimination for activity types.
• Inference latency: on-edge, $\leq$100 ms per 1 s window.

4. User Experience and Interface Mapping to the 4Ms

Comprehensive incorporation of the 4Ms domains into the user interface (UI) ensures accessibility and relevance for both caregivers and older adults:

• Matters Most: Configurable dashboards allow selection of prioritized events (e.g., missed doses, inactivity). Privacy is assured—no audio or video, vibration sensing only.
• Mentation: Sensor placement guided through icon-based instructions; LLM-generated queries adapt complexity based on user cognitive profile; most inputs are yes/no or multiple choice.
• Mobility: 24-hour timelines visualize walking, transitions, and bathroom usage, with longitudinal trend analysis for detection of changes in functional ability.
• Medication: Pill-dose log with real-time markers and automated reminders if dosing events are missed. Example caregiver summary: “Yesterday: 3 of 4 scheduled med events detected. Next dose in 2 h. Bed-exit detected at 3:12 AM → potential fall risk.”

5. LLM-Assisted Deployment and Privacy Schema

Deployment logistics leverage a cloud-based LLM agent:

• Edge Hub ↔ Cloud: Only encrypted metadata (sensor state, environment schema) traverses the network via Wireguard tunnel.
• LLM Workflow:

1. Elicit environmental and furniture schema.
2. Query user/caregiver device placement preferences.
3. Propose $N$ placements $\left\{(loc_i, gain_i, orientation_i)\right\}$.
4. Score placements: $PerfScore_i \in [0,1]$, $UXScore_i \in [0,1]$.
5. Select $\arg\max_i (PerfScore_i + UXScore_i)$ or, algorithmically,

   $$\text{recommendation} \leftarrow \arg\max_{i \in [1...N]} \big[ \alpha \cdot PerfScore(i) + (1-\alpha) \cdot UXScore(i) \big],\ \alpha = 0.5$$

• Privacy & Plug-and-Play: All vibration data remains local. Only summary statistics and health diagnostics are uploaded. No network port forwarding or manual router configuration is needed; setup is limited to plugging in the edge hub.

6. Deployment Insights, Challenges, and Best Practices

Critical findings and procedural recommendations from empirical deployment are as follows:

• Discreet, integrated hardware enables acceptance, especially for users with cognitive impairment.
• On-site, scripted sensor checks are essential for establishing adequate SNR before active deployment.
• LLM-assisted configuration enables non-expert caregivers to set up systems yielding expert-level performance.
• Domain shift is mitigated via cross-modal pretraining (audio-to-vibration) and rapid in-home fine-tuning.
• Lightweight UDP with parity + local buffering addresses network congestion.
• Ground truthing is limited in real homes (short video sessions or prompts), but systematic co-design with geriatric and HCI experts optimizes ecological validity.
• Best practices include edge inference for privacy, structured schemas and LLMs for plug-and-play usability, and regular remote updates and health monitoring.

In summary, the Geriatric 4Ms Framework, as instantiated in remote monitoring system design, supports the systematic translation of person-centered priorities into actionable, measurable, and privacy-preserving care models integrating hardware, machine learning, user experience, and deployment logistics (Lee et al., 24 Jan 2026).